Editing IPCC:AR6/WGI/Chapter-8 (section)

== 8.4 What Are the Projected Water Cycle Changes? ==

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We consider global and regional climate projections of the water cycle, assessing projected changes in each component of the water cycle ( [[#8.4.1|Section 8.4.1]] ) and the global-scale and regional phenomena that directly impact it ( [[#8.4.2|Section 8.4.2]] ).

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=== 8.4.1 Projected Water Cycle Changes ===

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Most projected changes in the water cycle are not expected to be uniform in space or time. They are driven by both dynamical and thermodynamical processes ( [[#8.2|Section 8.2]] ) and have not necessarily emerged yet in the recent observational record ( [[#8.3|Section 8.3]] ) as they are superimposed on substantial natural fluctuations in weather and climate. Therefore, projecting regional water cycle changes remains challenging. However, a number of physically understood responses can be evaluated using both CMIP5 and CMIP6 models, which are important for guiding decision making that anticipates, prepares for, and responds to water cycle changes. In this section, global maps of projected changes in water cycle variables are assessed using the WGI AR6 ‘simple method’ (see Cross-Chapter Box Atlas1), which uses hatching to highlight where less than 80% of the models agree on the sign of projected changes. This choice differs from [[IPCC:Wg1:Chapter:Chapter-4#4.2.6|Section 4.2.6]] for a number of reasons. These include the weak signal-to-noise ratio of projected hydrological changes in low to medium emissions scenarios, the sensitivity of their statistical significance to the baseline reference period, and the non-Gaussian distribution of many water cycle variables (see Cross-Chapter Box Atlas.1 for more details on strengths and limitations of the hatching methods implemented within AR6).

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==== 8.4.1.1 Global Water Cycle Intensity and P–E Over Land and Oceans ====

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As discussed in 8.3.1.1, the definition of global water cycle intensity varies from the simple metric of increases in global mean precipitation to broader joint considerations of water vapour and its transport, precipitation minus evaporation (P–E) rates and continental runoff (Figure 8.1). The AR5 determined that globally averaged precipitation is ''virtually certain'' to increase with temperature and that there is ''high confidence'' that the contrast of annual mean precipitation between dry and wet regions and seasons will increase over most of the globe as temperatures and moisture transports increase ( [[#Collins--2013|Collins et al., 2013]] ). The AR5 also highlighted that continued ocean warming for a few decades after GHG forcing stabilizes or begins to decrease will also lead to further increases in global mean precipitation and evaporation.

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'''Table 8.1 |''' '''Global and global land annual mean water cycle projections in the mid-term''' ( '''2041–2060''' ''') and long term''' ( '''2081–2100''' ''') relative to present day''' ( '''1995–2014''' '''), showing present day mean and 90% confidence range across CMIP6 models (historical experiment) and projected mean changes and the 90% confidence range across the same set of models and a range of Shared Socio-economic Pathway scenarios.''' Note that the exact value of changes can vary slightly based on the number of models assessed, but not sufficiently to affect the assessment. Further details on data sources and processing are available in the chapter data table (Table 8.SM.1).

{| class="wikitable"
|-
|
| colspan="5"| '''Mid-term:''' '''2041–2060''' '''Minus Reference Period'''

| colspan="5"| '''Long Term:''' '''2081–2100''' '''Minus Reference Period'''

|-
|
| '''1995''' – '''2014''' '''reference period'''

| '''SSP1-1.9'''

| '''SSP1-2.6'''

| '''SSP2-4.5'''

| '''SSP3-7.0'''

| '''SSP5-8.5'''

| '''SSP1-1.9'''

| '''SSP1-2.6'''

| '''SSP2-4.5'''

| '''SSP3-7.0'''

| '''SSP5-8.5'''

|-
| colspan="12"| '''Global Annual'''

|-
| Precipitation (mm day <sup>–1</sup> )

| 2.96 [2.76 to 3.17]

| 0.06 [0.03 to 0.11]

| 0.07 [0.03 to 0.12]

| 0.07 [0.04 to 0.12]

| 0.06 [0.03 to 0.11]

| 0.08 [0.03 to 0.14]

| 0.06 [0.02 to 0.11]

| 0.09 [0.04 to 0.17]

| 0.12 [0.07 to 0.21]

| 0.15 [0.08 to 0.24]

| 0.2 [0.1 to 0.33]

|-
| Precipitable Water (kg m <sup>2</sup> )

| 24.79 [23.06 to 26.82]

| 1.42 [0.7 to 2.26]

| 1.84 [1.03 to 2.62]

| 2.29 [1.6 to 3.09]

| 2.7 [1.92 to 3.92]

| 3.15 [2.13 to 4.38]

| 1.11 [0.28 to 2.13]

| 2.11 [0.98 to 3.15]

| 3.76 [2.41 to 5.08]

| 6.2 [4.24 to 8.83]

| 7.92 [5.21 to 10.69]

|-
| colspan="12"| '''Global Land Annual'''

|-
| Precipitation (mm day <sup>–1</sup> )

| 2.27 [1.98 to 2.58]

| 0.07 [0.02 to 0.11]

| 0.07 [–0.0 to 0.13]

| 0.06 [0.01 to 0.13]

| 0.06 [0.02 to 0.12]

| 0.09 [0.01 to 0.16]

| 0.06 [0.01 to 0.1]

| 0.08 [0.02 to 0.16]

| 0.11 [0.02 to 0.19]

| 0.14 [0.03 to 0.22]

| 0.2 [0.07 to 0.32]

|-
| Precipitation – Evaporation (mm day <sup>–1</sup> )

| 0.87 [0.49 to 1.26]

| 0.02 [0.0 to 0.03]

| 0.02 [–0.01 to +0.05]

| 0.02 [–0.02 to +0.06]

| 0.03 [–0.0 to +0.06]

| 0.04 [0.0 to 0.1]

| 0.01 [–0.0 to +0.03]

| 0.03 [–0.01 to +0.08]

| 0.04 [–0.01 to 0.07]

| 0.07 [0.0 to 0.12]

| 0.1 [0.01 to 0.22]

|-
| Runoff (mm day <sup>–1</sup> )

| 0.79 [0.54 to 1.0]

| 0.02 [0.0 to 0.05]

| 0.04 [–0.0 to +0.1]

| 0.04 [–0.0 to +0.11]

| 0.04 [0.01 to 0.08]

| 0.06 [0.01 to 0.14]

| 0.02 [–0.0 to +0.03]

| 0.04 [–0.0 to +0.13]

| 0.06 [0.0 to 0.17]

| 0.1 [0.02 to 0.2]

| 0.15 [0.04 to 0.27]

|-
| Precipitable Water (kg m <sup>2</sup> )

| 18.86 [17.12 to 21.28]

| 1.23 [0.57 to 1.96]

| 1.58 [0.77 to 2.42]

| 1.96 [1.34 to 2.76]

| 2.33 [1.63 to 3.46]

| 2.72 [1.79 to 3.84]

| 0.95 [0.19 to 1.95]

| 1.78 [0.8 to 2.77]

| 3.18 [2.04 to 4.34]

| 5.33 [3.57 to 7.5]

| 6.81 [4.35 to 9.32]

|}

In this Report, [[IPCC:Wg1:Chapter:Chapter-4|Chapter 4]] provides an updated assessment of global annual precipitation ( [[IPCC:Wg1:Chapter:Chapter-4#4.3.1|Section 4.3.1]] ), finding that it is ''very likely'' that annual precipitation averaged over all land regions continuously increases as global surface temperatures increase in the 21st century ( ''high confidence'' ). CMIP6 projections for long-term changes in P–E (Figure 8.13) show that, for all scenarios, P–E increases over the tropics and high latitudes and decreases over the subtropics, resulting from a thermodynamically driven amplification of P–E patterns ( [[#8.2.2.1|Section 8.2.2.1]] ). Both the intensity of changes and the spread among the models is larger for the higher emissions scenarios. A less coherent latitudinal pattern and smaller magnitude of P–E changes over land reflect the complex influence of land–ocean warming contrast, atmospheric circulation change and vegetation feedbacks ( [[#8.2.2.1|Section 8.2.2.1]] ). However, stronger atmospheric moisture transport, increases in precipitation and evaporation over global land and ocean and larger continental runoff that is in part fed by melting of glaciers characterizes a more intense water cycle with global warming.

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[[File:283dc39a1b9cb529b8d3232957d5976f IPCC_AR6_WGI_Figure_8_13.png]]

'''Figure 8.13 |''' '''Zonal and annual-mean projected long-term changes in the atmospheric water budget.''' Zonal and annual mean projected changes (mm day <sup>–1</sup> ) in P (precipitation, left column), E (evaporation, middle column), and P–E (right column) over both land and ocean areas (coloured lines) and over land only (black lines) averaged across available CMIP6 models (number provided at the top left of each panel) in the SSP1-2.6 (top row), SSP2-4.5 (middle row) and SSP5-8.5 (bottom row) scenario, respectively. Shading denotes confidence intervals estimated from the CMIP6 ensemble under a normal distribution hypothesis. Colour shading denotes changes over both land and ocean. Grey shading represents internal variability derived from the pre-industrial control simulations. All changes are estimated for 2081–2100 relative to the 1995–2014 base period. Further details on data sources and processing are available in the chapter data table (Table 8.SM.1).

Global and global land mean water cycle changes from CMIP6 projections are shown in Table 8.1. Increases in global and continental precipitation, P–E and runoff in both the mid-term and long-term illustrate the future intensification of the water cycle, with the magnitude of change increasing with emissions scenarios. Consistent with AR5, CMIP6 simulations of global mean precipitation show a systematic multi-model mean increase of 1.6 to 2.9 % °C <sup>–1</sup> warming (apparent hydrological sensitivity; [[#8.2.1|Section 8.2.1]] ) by 2081 – 2100 relative to present day across the new SSP scenarios (using global surface air temperature change from Table 4.1). It is well understood that rising concentrations of CO <sub>2</sub> drive a long-term increase in global precipitation with warming, but with the increase partly offset by rapid atmospheric adjustments to the direct atmospheric heating from radiative forcing agents ( [[#8.2.1|Section 8.2.1]] ). The largest apparent hydrological sensitivity is found for SSP1-1.9, where the suppressing effects on precipitation from atmospheric heating by greenhouse gases (GHGs) rapidly reduce as their concentration falls. Additional warming due to reduced aerosol loadings under the SSP scenarios (Lund et al. , 2019) further increases global precipitation ( [[#Rotstayn--2013|Rotstayn et al., 2013]] ; [[#Wu--2013|Wu et al., 2013]] ; [[#Salzmann--2016|Salzmann, 2016]] ; [[#Richardson--2018|T.B. Richardson et al., 2018]] b; [[#Samset--2018b|Samset et al., 2018b]] ; [[#Westervelt--2018|Westervelt et al., 2018]] ), with particularly strong contributions from increased monsoon rainfall over East and South Asia ( [[#Levy--2013|Levy et al., 2013]] ; [[#Westervelt--2015|Westervelt et al., 2015]] ; [[#Dwyer--2017|Dwyer and O’Gorman, 2017]] ).

Over global land there is a small range in global mean multi-model mean precipitation increase across scenarios in the mid-term (2.6 – 4.0 %), which widens (to 2.6 – 8.8 %) in the long-term (Table 8.1). The long-term projections are consistent with the [[IPCC:Wg1:Chapter:Chapter-4|Chapter 4]] assessment that global annual precipitation over land is projected to increase on average by 2.4 [ – 0.2 to +4.7] % ( ''likely'' range) in the SSP1-1.9 low-emissions scenario and by 8.3 [0.9 to 12.9] % in the SSP5-8.5 high emissions scenario by 2081–2100 relative to 1995–2014. Small differences in assessed model mean changes in Chapter 4, Table 4.2 result from a slightly different set of models considered for Table 8.1. Over land, P–E increases by around 2 – 3% in the mid-term (apart from SSP5-8.5 where increases are almost 5%) and around 1 – 12% in the long-term, determined by increased moisture transport from the ocean to land [[#8.4.1.2|Section 8.4.1.2]] ). Runoff increases are larger and less certain due to additional inputs from glacier melt and changes in groundwater storage ( [[#8.4.1.7|Section 8.4.1.7]] ). Overall, precipitation and runoff are ''very likely'' to increase over the global land in all scenarios in the mid- and long term. P–E is ''likely'' to increase over global land in the mid- and long term and ''very likely'' in SSP1-1.9, SSP3-7.0 and SSP5-8.5 pathways. The mid-term consistency in projections across scenarios is not apparent for precipitable water vapour, which increases over land by around 6 – 15% in the mid-term and 5 – 36% in the long-term across all scenarios. This implies that increases in extreme precipitation (closely related to atmospheric water vapour content; [[#8.2.3.2|Section 8.2.3.2]] ) are dependent on mitigation pathway, even in the mid-term ( [[IPCC:Wg1:Chapter:Chapter-11#11.4.5|Section 11.4.5]] ). Water vapour residence time (computed as the ratio of precipitable water vapour to precipitation from values in Table 8.1) increases from eight days in the present to nine days in mid-term and up to about ten days in the long-term over land in SSP3-7.0, indicating a longer time to moisten the atmosphere between precipitation events. The CMIP6 projections are therefore consistent with an intensification but not acceleration of the global wa ter cycle.

In summary, it is ''virtually certain'' that global water cycle intensity, considered in terms of global and continental mean precipitation, evaporation and runoff, will increase with continued global warming. Global annual precipitation over land is projected to increase on average by 2.4 [–0.2 to +4.7] % ( ''likely'' range) in the SSP1-1.9 low-emissions scenario and by 8.3 [0.9 to 12.9] % in the SSP5-8.5 high emissions scenario by 2081–2100 relative to 1995–2014.

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==== 8.4.1.2 Water Vapour and Its Transport ====

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Globally, AR5 assessed that by the end of the 21st century, the average quantity of water vapour in the atmosphere could increase by 5–25%, depending on emissions. The AR5 assessed that increases in near-surface specific humidity over land are ''very likely'' , but that it was also ''likely'' that near-surface relative humidity would decrease over many land areas, although with only ''medium confidence'' . In terms of moisture transport, AR5 assessed that it was ''likely'' that moisture transport into the high latitudes would increase and that there was ''high confidence'' that, over the ocean, atmospheric moisture transport from the evaporative regions to the wet regions would increase.

CMIP6 climate models continue to project a steady increase in global mean column-integrated water vapour by around 6 – 13% by 2041 – 2060 and 5 – 32% by 2081 – 2100, depending on scenario (Table 8.1). This is consistent with projected atmospheric warming ( [[IPCC:Wg1:Chapter:Chapter-4#4.5.1.2|Section 4.5.1.2]] ) and the Clausius–Clapeyron relationship ( [[#8.2.1|Section 8.2.1]] ) where every degree Celsius of warming is associated with an approximate 7% increase in atmospheric moisture in the lower atmospheric layers where most of the water vapour is concentrated. This increase sustains a positive feedback on anthropogenic global warming ( [[IPCC:Wg1:Chapter:Chapter-7#7.4.2.2|Section 7.4.2.2]] ). In contrast, the response of clouds is much more spatially heterogeneous, microphysically complex, and model-dependent so that the projected cloud feedbacks remain a key uncertainty for constraining climate sensitivity ( [[IPCC:Wg1:Chapter:Chapter-7#7.4.2.4|Section 7.4.2.4]] ).

CMIP6 models project an overall decrease in near-surface relative humidity over land, although with some regional and seasonal variations in their response (Figure 4.26). Regional changes in near-surface humidity over land are dominated by thermodynamic processes and are primarily controlled by moisture transport from the warming ocean ( [[#Chadwick--2016a|Chadwick et al., 2016a]] ). Increases in specific humidity lower than the thermodynamic rate are explained by greater warming over land than ocean and modulated by land – atmosphere feedbacks such as soil moisture and plant stomatal changes ( [[#8.2.2.1|Section 8.2.2.1]] ; [[#Berg--2017|Berg et al., 2017]] ; [[#Douville--2020|Douville et al., 2020]] ). This explains why climate models continue to project a contrasting response of near-surface relative humidity, with a slight and possibly overestimated increase over the oceans and a consistent but possibly underestimated decrease over land ( [[#Byrne--2016|Byrne and O’Gorman, 2016]] ; [[#Douville--2017|Douville and Plazzotta, 2017]] ; R. [[#Zhang--2018|]] [[#Zhang--2018|]] [[#Zhang--2018|]] [[#Zhang--2018|]] [[#Zhang--2018|Zhang et al., 2018]] ).

While projections of water vapour are well understood due to the constraints of the Clausius–Clapeyron relationship, projections of water vapour transport are complicated regionally by the role of changes in the wind field, which is influenced by a wide variety of factors. Additionally, there has been relatively little general evaluation of moisture transport in models. In CMIP5 models, both the mean and variability of the vertically-integrated moisture transport is projected to increase, largely due to increases in water vapour ( [[#Lavers--2015|Lavers et al., 2015]] ), with substantial regional differences ( [[#Levang--2015|Levang and Schmitt, 2015]] ). Single-model studies have illustrated projected increases in low-altitude moisture transport into convergence regions ( [[#Allan--2014|Allan et al., 2014]] ) and from ocean to land ( [[#Zahn--2013|Zahn and Allan, 2013]] ) that are consistent with present day trends. Increases in moisture transport have been linked to increases in large precipitation accumulations over land ( [[#Norris--2019|Norris et al., 2019]] ). Based on robust physics and supported by modelling studies, it is well understood that moisture transport increases into convergent parts of the atmospheric circulation such as storm systems, the tropical rain belt and high latitudes ( [[#8.2.2.1|Section 8.2.2.1]] ), but changes in atmospheric circulation that are less well understood alter moisture transport regionally ( [[#8.2.2.2|Section 8.2.2.2]] ). Therefore, given the limited examination of moisture transport in models, regional projections should be considered with caution. Changes in moisture transport specifically associated with monsoons, atmospheric rivers, and other specific circulation features are discussed further in the following sections.

In summary, there is ''high confidence'' in continued increases in global mean column integrated water vapour and near-surface specific humidity over land. There is ''medium confidence'' in region and season-dependent decreases in near-surface relative humidity over land, due to the complex physical processes involved ''.'' In general, there will be increases in moisture transport into storm systems, monsoons and high latitudes ( ''medium co'' ''nfidence'' ).

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==== 8.4.1.3 Precipitation Amount, Frequency and Intensity ====

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This section assesses projected changes in precipitation at regional scales. Note that changes in precipitation seasonality are assessed in Box 8.2 and that changes in regional monsoons are assessed in [[#8.4.2.4|Section 8.4.2.4]] , where both circulation and rainfall are considered. Further assessments of regional projections of precipitation are presented in Chapters 10, 12 and the Atlas, while a comprehensive assessment of changes in precipitation extremes is provided in Chapter 11.

The AR5 assessed that the contrast of mean precipitation amount between dry and wet regions and seasons is expected to increase over most of the globe as temperatures increase ( ''high confidence'' ), but with large regional variations. Precipitation over the high latitudes, equatorial Pacific Ocean, mid-latitude wet regions, and monsoon regions were assessed as ''likely'' to increase under the RCP8.5 scenario, and in many mid-latitude and subtropical dry regions as ''likely'' to decrease (AR5 Chapters 7, 12, and 14). Extreme precipitation over most mid-latitude land areas and wet tropical regions was assessed as ''very likely'' to become more intense and more frequent.

Geographical patterns of projected precipitation changes show substantial seasonal contrasts and regional differences, including over land (Figure 8.14 and Figure 4.27). Projections for 2081 – 2100 under the SSP2-4.5 scenario suggest increased precipitation over the tropical oceans, north-eastern Africa, the Arabian Peninsula, India, south-eastern Asia and the Polar regions while decreased precipitation is projected mainly over the subtropical regions ( [[IPCC:Wg1:Chapter:Chapter-4#4.5.1.4|Section 4.5.1.4]] ). Precipitation changes contrast regionally in the tropics with wetter wet seasons over South Asia, central Sahel and eastern Africa, but less precipitation over Amazonia and coastal West Africa ( [[#8.4.2.4|Section 8.4.2.4]] ). These large-scale responses are associated with stronger moisture transports in a warmer climate that are modulated by the greater warming over land than ocean, atmospheric circulation responses and land surface feedbacks ( [[#8.2.2|Section 8.2.2]] ). There is agreement across CMIP5 and CMIP6 modelling studies that precipitation increases in wet parts of the atmospheric circulation and decreases in dry parts ( [[#Liu--2013|Liu and Allan, 2013]] ; [[#Kumar--2015|Kumar et al., 2015]] ; [[#Deng--2020|Deng et al., 2020]] ; [[#Schurer--2020|Schurer et al., 2020]] ) although these regions shift with atmospheric circulation changes. The overall pattern is robust across different model scenarios and time horizons ( [[#Tebaldi--2018|Tebaldi and Knutti, 2018]] ), but some deviations from the mean pattern cannot be excluded due to the multiple time scales and non-linear atmospheric or land surface processes involved ( [[#8.5.3|Section 8.5.3]] ). Near-term regional changes in precipitation are more uncertain because of a stronger sensitivity to natural variability ( [[#8.5.2|Section 8.5.2]] ) and non-GHG anthropogenic forcings ( [[IPCC:Wg1:Chapter:Chapter-4#4.4.1.3|Section 4.4.1.3]] and 8.4.3.1).

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[[File:4274eba6c74a1dd9f72d36fd37f4bc65 IPCC_AR6_WGI_Figure_8_14.png]]

'''Figure 8.14 |''' '''Projected long-term relative changes in seasonal mean precipitation.''' Global maps of projected relative changes (%) in seasonal mean of precipitation averaged across available CMIP6 models (number provided at the top right of each panel) in the SSP2-4.5 scenario. All changes are estimated for 2081–2100 relative to the 1995–2014 base period. Uncertainty is represented using the simple approach. No overlay indicates regions with high model agreement, where ≥80% of models agree on sign of change; diagonal lines indicate regions with low model agreement, where &lt;80% of models agree on sign of change. For more information on the simple approach, please refer to the Cross-Chapter Box Atlas.1. Further details on data sources and processing are available in the chapter data table (Table 8.SM.1).

Projected changes in regional precipitation also arise as a response to changes in large-scale atmospheric circulation ( [[#8.2.2.2|Section 8.2.2.2]] and 8.4.2), both in the tropics (Chadwick et al. , 2016b; Byrne et al. , 2018) and extratropics ( [[#Shaw--2019|Shaw, 2019]] ; [[#Oudar--2020b|Oudar et al., 2020b]] ). Despite variability in simulated changes, CMIP5 climate models consistently project large rainfall changes (of varying sign) over considerable proportions of tropical land during the 21st century ( [[#Chadwick--2016b|Chadwick et al., 2016b]] ). Since AR5, some robust responses in large-scale circulation patterns have been identified. For example, and as further assessed in [[#8.4.2|Section 8.4.2]] , CMIP6 models project a northward shift in the tropical rain belt over eastern Africa and the Indian Ocean and a southward shift in the eastern Pacific and Atlantic oceans ( [[#Mamalakis--2021|Mamalakis et al., 2021]] ). A projected strengthening and tightening of the tropical rain belt increases the contrasts between wet and dry tropical weather regimes and seasons. It is less clear how the well understood poleward expansion of the subtropics and mid-latitude storm tracks influences precipitation over subtropical and mid-latitude continents ( [[#8.2.2.2|Section 8.2.2.2]] ).

An ensemble of 31 CMIP6 models under the SSP5-8.5 scenario projects increases precipitation by 10–30% over much of the USA and decreases by 10–40% over Central America and the Caribbean by 2080 – 2099 ( [[#Almazroui--2021|Almazroui et al., 2021]] ). This CMIP6 ensemble also projects an increase in annual precipitation over the southern Arabian Peninsula and a decrease over the northern Arabian Peninsula, as also projected by CMIP3 and CMIP5 models ( [[#Almazroui--2020a|Almazroui et al., 2020a]] ). Annual mean precipitation is projected to increase over South Asia during the 21st century under all scenarios, although the rate of change varies within the region based on 27 CMIP6 models ( [[#Almazroui--2020c|Almazroui et al., 2020c]] ). CMIP6 projections also display a reduction in annual mean precipitation over northern and southern Africa while increases are projected over Central Africa, under the SSP1-2.6, SSP2-4.5 and SSP5-8.5 scenarios ( [[#Almazroui--2020b|Almazroui et al., 2020b]] ). The AR6 ( [[IPCC:Wg1:Chapter:Atlas|Atlas]] assesses that regions where annual mean rainfall is ''likely'' to increase include the Ethiopian Highlands, East, South and North Asia, south-eastern South America, northern Europe, northern and eastern North America, and the Polar Regions. In contrast, regions where annual mean rainfall is ''likely'' to decrease include southern Africa, coastal West Africa, Amazonia, south-western Australia, Central America, south-western South America, and the Mediterranean.

The AR5 identified that high-latitude precipitation increase may lead to an increase in snowfall in the coldest regions and a decrease of snowfall in warmer regions due to a decreased number of freezing days. The fraction of precipitation falling as snow and the duration of snow cover was projected to decrease. Heavy snowfall events globally are not expected to decrease significantly with warming as they occur close to the water freezing point, which will migrate poleward and in altitude ( [[#O’Gorman--2014|O’Gorman, 2014]] ; [[#Turner--2019|Turner et al., 2019]] ). There are only a small number of studies evaluating the implications of this mechanism in specific regions. A study for the north-eastern USA indicates smaller reductions for major snowfall events against the broader decline in snowfall expected from thermodynamic effects ( [[#Bintanja--2017|Bintanja and Andry, 2017]] ). Arctic snowfall is projected to decrease as rainfall makes up more of the precipitation ( [[#Zarzycki--2018|Zarzycki, 2018]] ).

Beyond annual or seasonal mean precipitation amounts, an implication of the parallel intensification of the global water cycle and of the increased residence time of atmospheric water vapour ( [[#8.2.1|Section 8.2.1]] ) is that the distribution of daily and sub-daily precipitation intensities will experience significant changes ( [[#Pendergrass--2014b|Pendergrass and Hartmann, 2014b]] ; [[#Pendergrass--2015|Pendergrass et al., 2015]] ; [[#Bador--2018|Bador et al., 2018]] ; [[#Douville--2021|Douville and John, 2021]] ), with fewer but potentially stronger events ( ''high confidence'' ) ( [[IPCC:Wg1:Chapter:Chapter-4#4.3.3|Section 4.3.3]] ). CMIP6 projections show that in the long-term more drier days but more intense single events of precipitation are expected, regardless of scenario (Figure 8.15). Over almost all land regions, it is ''very likely'' that extreme precipitation will intensify at a rate close to the 7% °C <sup>–1</sup> of global warming, but with large spatial differences (Sections 11.4 and 8.2.3.2). The projected increase in precipitable water is expected to lead to an increase in the highest possible precipitation intensities and an increase in the probability of occurrence of extreme precipitation events on the global scale (Neelin et al., 2017), regardless of how annual-mean precipitation changes ( [[#O’Gorman--2009|O’Gorman and Schneider, 2009]] ; [[#O’Gorman--2015|O’Gorman, 2015]] ). The projected increase in heavy precipitation intensity is also found for daily mean precipitation intensity though at a lower rate ( [[#Pendergrass--2014a|Pendergrass and Hartmann, 2014a]] ).

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[[File:44b811470e377c850a5783ebbff7e125 IPCC_AR6_WGI_Figure_8_15.png]]

'''Figure 8.15 |''' '''Projected long-term relative changes in daily precipitation statistics.''' Global maps of projected seasonal mean relative changes (%) in the number of dry days (i.e., days with less than 1 mm of rain) and daily precipitation intensity (in mm day – 1 , estimated as the mean daily precipitation amount at wet days – for example, days with intensity above 1 mm day – 1 ) averaged across available CMIP6 models (number provided at the top right of each panel) in the SSP1-2.6 '''(a, b)''' , SSP2-4.5 '''(c, d)''' and SSP5-8.5 '''(e, f)''' scenario respectively. Uncertainty is represented using the simple approach. No overlay indicates regions with high model agreement, where ≥80% of models agree on sign of change; diagonal lines indicate regions with low model agreement, where &lt;80% of models agree on sign of change. For more information on the simple approach, please refer to the Cross-Chapter Box Atlas.1. Further details on data sources and processing are available in the chapter data table (Table 8.SM.1).

An increase in the number of dry days is also projected in several regions of the world ( [[#Polade--2014|Polade et al., 2014]] ; [[#Berthou--2019a|Berthou et al., 2019a]] ), which can dominate the annual precipitation change at least in the subtropics ( [[#Polade--2014|Polade et al., 2014]] ; [[#Douville--2021|Douville and John, 2021]] ). These findings are supported by CMIP6 projections showing a widespread increase in daily mean precipitation intensity over land (Figure 8.15b,d,f) as well as an increase in the number of dry days in the subtropics and over Amazonia and Central America (Figure 8.15a,b,c). Such changes in precipitation regimes, as well as the general increase in the frequency and intensity of precipitation extremes ( [[IPCC:Wg1:Chapter:Chapter-11#11.4.5|Section 11.4.5]] ), contribute to an overall increase in precipitation variability ( [[#Polade--2014|Polade et al., 2014]] ; [[#Pendergrass--2017|Pendergrass et al., 2017]] ; [[#Douville--2021|Douville and John, 2021]] ). This is also found in CMIP6 models, which show a stronger increase of interannual variability than in seasonal mean precipitation changes, apart from in the winter extratropics where both quantities increase at the same rate with increasing global warming levels (Figure 8.16).

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[[File:f1c36cb4c37a18ef50999b4d59b8008e IPCC_AR6_WGI_Figure_8_16.png]]

'''Figure 8.16 |''' '''Rate of change in components of water cycle mean and variability across increasing global warming levels.''' Relative change (%) in seasonal mean total precipitable water (grey line), precipitation (red solid lines), runoff (blue solid lines), as well as in standard deviation of precipitation (red dashed lines) and runoff (blue dashed lines) averaged over extratropical land in '''(c)''' summer and '''(d)''' winter, and tropical land in '''(a)''' June–July–August (JJA) and '''(b)''' December–January–February (DJF) as a function of global mean surface temperature for the CMIP6 multi-model mean across the SSP5-8.5 scenario. Extratropical winter refers to DJF for Northern Hemisphere and JJA for Southern Hemisphere (and the reverse for extratropical summer). Each marker indicates a 21-year period centred on consecutive decades between 2015 and 2085 relative to the 1995–2014 base period. Precipitation and runoff variability are estimated by their standard deviation after removing linear trends from each time series. Error bars show the 5–95% confidence interval for the warmest 5°C global warming level. Figure adapted from [[#Pendergrass--2017|Pendergrass et al. (2017)]] and updated with CMIP6 models. Further details on data sources and processing are available in the chapter data table (Table 8.SM.1).

In summary, it is ''virtually certain'' that global precipitation will increase with warming due to increases in GHG concentrations and decreases in air pollution. There is ''high confidence'' that total precipitation will increase in the high latitudes, with a shift from snowfall to rainfall except in the coldest regions and seasons. There is also ''high confidence'' that precipitation will decrease over the Mediterranean, southern Africa, Amazonia, Central America, south-western South America, south-western Australia and coastal West Africa and that monsoon precipitation will increase over South Asia, East Asia and central-eastern Sahel. See ( [[#8.4.2.4|Section 8.4.2.4]] for a more detailed assessment of changes in regional monsoons. Daily mean precipitation intensities, including extremes, are projected to increase over most regions ( ''high confidence'' ). The number of dry days is projected to increase over the subtropics, Amazonia, and Central America ( ''medium confidence'' ). There is ''high confidence'' in an overall increase in precipitation variability over most land areas.

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'''Box 8.2 | Changes in Water Cycle S''' '''easonality'''

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'''Observed changes'''

The AR5 did not highlight observed changes in water cycle seasonality and SRCCL mostly emphasized changes in vegetation seasonality. Since AR5, a number of relevant studies have been published, but often with conflicting results. Based on three ''in situ'' datasets, reduced precipitation seasonality was identified over 62% of the terrestrial ecosystems analysed from 1950 – 2009 (Murray-Tortarolo et al. 2017). In contrast, both ''in situ'' and satellite data show a general increase in the annual range of precipitation from 1979 to 2010, which is dominated by wetter wet seasons ( [[#Chou--2013|Chou et al., 2013]] ). This paradox may be partly explained by a larger aerosol radiative forcing in the middle of the 20th century as well as by internal variability ( [[#Kumar--2015|Kumar et al., 2015]] ; see also Box 8.1). For instance, the ‘long rains’ over East Africa experienced declining trends in the 1980s and 1990s ( [[#Nicholson--2017|Nicholson, 2017]] ), which was linked to anthropogenic aerosols and SST patterns ( [[#Rowell--2015|Rowell et al., 2015]] ), followed by a recent recovery that was linked to internal variability ( [[#Wainwright--2019|Wainwright et al., 2019]] ). Two satellite datasets revealed decreased rainfall seasonality in the tropics but an increased seasonality in the subtropics and mid-latitudes since 1979, without clear attribution ( [[#Marvel--2017|Marvel et al., 2017]] ).

Large differences have been found across seven global precipitation datasets, with no region showing a consistent, statistically significant, positive or negative trend over the last three decades (X. [[#Tan--2020|]] [[#Tan--2020|Tan et al., 2020]] ). Regional studies suggest that observed changes in precipitation seasonality are neither uniform nor stable across the 20th century (X. Li et al. , 2016; [[#Mallakpour--2017|Mallakpour and Villarini, 2017]] ; Sahany et al. , 2018; Deng et al. , 2019) . Since the 1980s, there is growing evidence that contrasts between wet and dry regimes, including seasonality, have increased ( [[#Liu--2013|Liu and Allan, 2013]] ; Polson et al. , 2013; Murray-Tortarolo et al. , 2016; Tapiador et al. , 2016; Gallego et al. , 2017; [[#Polson--2017|Polson and Hegerl, 2017]] ; Barkhordarian et al. , 2018; Lan et al. , 2019; Liang et al. , 2020; Schurer et al., 2020) .

Additional changes in seasonality may manifest in the timing and duration of wet seasons. A later monsoon onset trend was reported throughout India from 1901 to 2013 ( [[#Sahany--2018|Sahany et al., 2018]] ). Conversely, an earlier rainfall onset was implicated in increased springtime rainfall over the Tibetan Plateau in recent decades (W. [[#Zhang--2017|Zhang et al., 2017]] a). Winter and early spring precipitation over the north-western Himalaya for the period 1951 – 2007 shows an increasing trend of daily precipitation extremes in association with enhanced amplitude variations of extratropical synoptic-scale systems known as ‘Western Disturbances’ (Madhura et al. , 2014; Cannon et al. , 2015; Krishnan et al. , 2019) . In China, an earlier onset was observed during 1961-2012 ( [[#Deng--2019|Deng et al., 2019]] ). In the African Sahel, rainfall has been most concentrated in the peak of the rainy season since the end of the 20th century ( [[#Biasutti--2019|Biasutti, 2019]] ). A shift in the seasonality of Sahelian rainfall, including delayed cessation has also been reported ( [[IPCC:Wg1:Chapter:Chapter-10#10.4.2.1|Section 10.4.2.1]] ; [[#Nicholson--2013|Nicholson, 2013]] ; [[#Dunning--2018|Dunning et al., 2018]] ). Over southern Africa, an observed earlier onset (1985 – 2007) is in contrast to a simulated historical and projected future delay in the wet season ( [[#Maidment--2015|Maidment et al., 2015]] ; [[#Dunning--2018|Dunning et al., 2018]] ). An increasingly early onset of the North American monsoon has been observed from 1978 to 2009 (Arias et al. , 2015) . Seasonality changes in the South American monsoon indicate delayed onsets since 1978 (Fu et al. , 2013; Yin et al. , 2014; Arias et al. , 2015; Debortoli et al. , 2015; Arvor et al. , 2017; Giráldez et al. , 2020; Haghtalab et al. , 2020; Correa et al., 2021).

In northern high latitudes, a shorter snow season (X. [[#Zeng--2018|Zeng et al., 2018]] ) is mainly due to an earlier onset of spring snowmelt ( [[#Peng--2013|Peng et al., 2013]] ) which has been attributed to anthropogenic climate change ( [[#Najafi--2016|Najafi et al., 2016]] ). Changes in snow seasonality affect streamflow at the regional scale, with an earlier peak in spring and a possible decrease of low-level flow in summer ( [[#Berghuijs--2014|Berghuijs et al., 2014]] ; [[#Kang--2016|Kang et al., 2016]] ; [[#Dudley--2017|Dudley et al., 2017]] ), while glacier shrinking can also alter the low-level flow in mountain catchments ( [[#Lutz--2014|Lutz et al., 2014]] ; [[#Milner--2017|Milner et al., 2017]] ; [[#Huss--2018|Huss and Hock, 2018]] ). This can be partly ameliorated by water management in regulated catchments ( [[#Arheimer--2017|Arheimer et al., 2017]] ), but not in large river basins such as the Amazon which also shows an increased seasonality of discharge since 1979 ( [[#Liang--2020|Liang et al., 2020]] ).

Increasing aridity contrasts between wet and dry seasons over the late 20th century have been suggested ( [[#Kumar--2015|Kumar et al., 2015]] ), with a human-induced decrease of water availability during the dry season over Europe, western North America, northern Asia, southern South America, Australia and eastern Africa ( [[#Padrón--2020|Padrón et al., 2020]] ). Seasonal contrasts in microwave surface soil moisture measurements have also increased over 1979 – 2016 ( [[#Pan--2019|Pan et al., 2019]] ). Terrestrial water storage variations derived from gravimetric measurements since 2003 show a strong seasonality which is underestimated by global hydrological models ( [[#Scanlon--2019|Scanlon et al., 2019]] ) and whose multi-decadal trends are difficult to interpret given the direct effect of enhanced water use ( [[#Rodell--2018|Rodell et al., 2018]] ; [[#Scanlon--2018|Scanlon et al., 2018]] ).

In summary, there is ''medium confidence'' that the annual range of precipitation has increased since the 1980s, at least in subtropical regions and over the Amazon. There is ''low confidence'' that this increase is due to human influence and that GHG forcing has already altered the timing or duration of wet seasons ''.'' There is ''high confidence'' that the human-induced retreat of the springtime snow cover and melting of glaciers have already contributed to changes in streamflow seasonality in high-latitude and low-elevation mountain catchments, and ''medium confidence'' that human activities have also contributed to an increased seasonality of water availability, including a drier dry season, in the extratropics.

'''Projected changes'''

The AR5 reported with ''high confidence'' that the contrast between wet and dry seasons will generally increase with global warming and that monsoon onset dates will ''likely'' become earlier or show little change, while monsoon retreat dates will ''likely'' be delayed, resulting in a lengthening of the wet season in many regions.

Since AR5, several studies have further documented a projected increase in rainfall seasonality and the understanding of the underlying mechanisms has been improved (Sections 8.2.1 and 8.3.2). CMIP5 models show that the seasonal concentration of annual precipitation will increase over many regions by the end of the 21st century, with robust model agreement in most subtropical regions where an increase in the mean number of dry days was also reported in the RCP8.5 scenario ( [[#Pascale--2016|Pascale et al., 2016]] ). The semi-arid, winter rainfall dominated subtropical climate is projected to shift poleward and eastward, with the equatorward margins replaced by a more arid climate type. However, evolving SST patterns and land – ocean warming contrasts cause more complex responses (Alessandri et al. , 2015; Polade et al. , 2017; Brogli et al. , 2019; Zappa et al. , 2020) . Projections over California show a stronger and shorter wet season ( [[#Polade--2017|Polade et al., 2017]] ; [[#Dong--2019|Dong et al., 2019]] ). Decreases in future winter and spring rainfall are projected over south-western Australia ( [[#Hope--2015|Hope et al., 2015]] ). Central Asia is projected to experience wetter winters, associated with an increase in snow depth in the north-eastern regions (Y. [[#Li--2019|]] [[#Li--2019|Li et al., 2019]] ). Even in a +2°C climate, both extreme precipitation and dryness will increase significantly in the extratropics, amplifying the seasonal precipitation range ( [[#Fujita--2019|Fujita et al., 2019]] ). A single-model study shows that the annual range of precipitation increases globally by 2.6% per 1°C of global warming in stabilized low-warming scenarios (Z. [[#Chen--2020|]] [[#Chen--2020|Chen et al., 2020]] a).

In the tropics, an amplified annual cycle (by about 3–5% °C <sup>–1</sup> ) of global land monsoon hydroclimates (precipitation '','' precipitation minus evaporation ( ''P–E'' ), and runoff) is projected by CMIP5 models under the RCP8.5 scenario, mostly due to a more intense wet season (W. [[#Zhang--2019|]] [[#Zhang--2019|Zhang et al., 2019]] b). A longer rainy season is projected by CMIP6 models over most regional monsoon areas except in the Americas ( [[#Moon--2020|Moon and Ha, 2020]] ). A delayed onset and cessation of the wet season over West Africa and the Sahel ( [[#Dunning--2018|Dunning et al., 2018]] ) and a slightly delayed onset of South Asian monsoon rainfall ( [[#Hasson--2016|Hasson et al., 2016]] ) are projected by CMIP5 models. CMIP5 projections suggest a strengthening of the annual cycle and a lengthening of the dry season in Southern Amazonia (Fu et al. , 2013; Reboita et al. , 2014; Boisier et al. , 2015; Pascale et al. , 2016; [[#Sena--2020|Sena and Magnusdottir, 2020]] ) . This is further verified by the projections from six CMIP6 models ( [[#Moon--2020|Moon and Ha, 2020]] ). A wet season shorter by 5 – 10 days by the end to the 21st century is projected for southern Africa ( [[#Dunning--2018|Dunning et al., 2018]] ).

An increase in streamflow seasonality is projected over several large rivers in the low-mitigation RCP8.5 scenario, but with only small changes in the seasonality timing, except in northern high latitudes due to the earlier but potentially slower snowmelt in a warmer world ( [[#Eisner--2017|Eisner et al., 2017]] ; [[#Musselman--2017|Musselman et al., 2017]] ). At the end of the century in a high-emissions scenario, peak snowmelt timing is projected to occur one month earlier and peak water volume is 79% lower in the eastern USA ( [[#Rhoades--2018|Rhoades et al., 2018]] ). Earlier snowmelt is projected, for example, by 30 days at the end of the 21st century in RCP4.5 for the Sierra Nevada in the western USA (F. [[#Sun--2018|]] [[#Sun--2018|Sun et al., 2018]] ). Sub-seasonal changes in water availability were found in many regions in the RCP8.5 scenario. However, these should be considered with caution given the magnitude of model errors (C.R. [[#Ferguson--2018|]] [[#Ferguson--2018|Ferguson et al., 2018]] ). Increases in the seasonality of water availability has been found to be more pronounced in areas with high atmospheric evaporative demand, giving rise to a pattern of seasonally variable regimes becoming even more variable ( [[#Konapala--2020|Konapala et al., 2020]] ). RCP4.5 and RCP8.5 projections show a pronounced soil drying in summer and autumn over western Europe, and a springtime drying over northern Europe due to an earlier snowmelt ( [[#Ruosteenoja--2018|Ruosteenoja et al., 2018]] ).

A simple relative seasonality metric ( [[#Walsh--1981|Walsh and Lawler, 1981]] ) applied to global projections based on CMIP6 models and SSP scenarios supports previous CMIP5 findings, especially the amplified seasonality of precipitation around the Mediterranean, and across southern Africa, California, southern Australia and the Amazon (Box 8.2, Figure 1). While such changes are not significant in the low-emissions SSP1-2.6 scenario, they are consistent with the increased frequency of dry days projected over the same regions (Figure 8.16). In monsoon regions outside the Americas, rainfall seasonality does not show a significant increase even in high-emissions scenarios. This challenges previous CMIP5 findings based on the difference between maximum and minimum monthly precipitation in a year (W. [[#Zhang--2019|]] [[#Zhang--2019|Zhang et al., 2019]] b) and higher sensitivity to the projected increase in precipitation extremes ( [[IPCC:Wg1:Chapter:Chapter-11#11.4.5|Section 11.4.5]] ). In the northern high latitudes, milder winters are associated with wetter conditions and a decrease in precipitation seasonality.

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[[File:2406f46bb99f532c09f4fe776aaa3b57 IPCC_AR6_WGI_Box_8_2_Figure_1.png]]

'''Box 8.2, Figure 1 |'''

'''Projected long-term changes in precipitation seasonality.''' Global maps of projected changes in precipitation seasonality (simply defined as the sum of the absolute deviations of mean monthly rainfalls from the overall monthly mean, divided by the mean annual rainfall as in [[#Walsh--1981|Walsh and Lawler, 1981]] ) averaged across available CMIP6 models (number provided at the top right of each panel) in the SSP1-2.6 '''(b)''' , SSP2-4.5 '''(c)''' and SSP5-8.5 '''(d)''' scenario respectively. The simulated 1995–2014 climatology is shown in panel '''(a)''' . All changes are estimated in 2081–2100 relative to 1995–2014. Uncertainty is represented using the simple approach. No overlay indicates regions with high model agreement, where ≥80% of models agree on sign of change. Diagonal lines indicate regions with low model agreement, where &lt;80% of models agree on sign of change. For more information on the simple approach, please refer to the Cross-Chapter Box Atlas.1. Further details on data sources and processing are available in the chapter data table (Table 8.SM.1).

In summary, the annual range of precipitation, water availability and streamflow will increase with global warming over subtropical regions and the Amazon ( ''medium confidence'' ), especially around the Mediterranean and across southern Africa ( ''high confidence'' ). The contrast between the wettest and driest month of the year is ''likely'' to increase by 3–5% °C <sup>–1</sup> with global warming in most monsoon regions, in terms of precipitation, water availability (P–E) and runoff ( ''medium confidence'' ). There is ''medium confidence'' that the monsoon season could be delayed in a warmer climate in the Sahel. There is ''high confidence'' of earlier snowmelt.

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<span id="evapotranspiration-1"></span>
==== 8.4.1.4 Evapotranspiration ====

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Since AR5, there is a growing body of evidence suggesting that future projections in evapotranspiration are driven by changes in temperature and relative humidity (Laîné et al. , 2014; Pan et al. , 2015; Ukkola et al. , 2016a) , as well as precipitation patterns, as found in AR5 .

Analysis of CMIP5 models suggests that atmospheric evaporative demand will increase over most areas of the world in high-emissions scenarios ( ''virtually certain'' ), mostly as a consequence of an increase in vapour pressure deficit ( [[#Scheff--2014|Scheff and Frierson, 2014]] , 2015; [[#Greve--2015|Greve and Seneviratne, 2015]] ; [[#Vicente-Serrano--2020|Vicente-Serrano et al., 2020]] ). CMIP5 models also project an increase in evapotranspiration over most land areas ( ''medium confidence'' ) ( [[#Laîné--2014|Laîné et al., 2014]] ). However, regional changes in evapotranspiration can also be influenced by changes in soil moisture and vegetation, which modulate the moisture flux from the land to the atmosphere. Several studies of CMIP5 projections suggest that increases in plant water use efficiency will limit or counteract rising evapotranspiration ( [[#Milly--2016|Milly and Dunne, 2016]] ; Swann et al. , 2016; Lemordant et al. , 2018; Y. Yang et al. , 2018) . However, other studies have found that transpiration increases due to the impact of climate change on growing season length, leaf area, and evaporative demand ( [[#8.2.3.3|Section 8.2.3.3]] ; Frank et al. , 2015; Mankin et al. , 2017, 2018, 2019; Guerrieri et al. , 2019; S. Zhou et al. , 2019; Vicente-Serrano et al. , 2020) . The parametrizations accounting for these complex physiological processes in global climate models may also be insufficient ( [[#Franks--2017|Franks et al., 2017]] ; [[#Peters--2018|Peters et al., 2018]] ; [[#Peano--2019|Peano et al., 2019]] ). Thus, there is currently ''low confidence'' in the role of vegetation physiology in modulating future projections of evapotranspiration.

CMIP6 models project a geographical pattern of changes in evapotranspiration similar to previous generation models (Figure 8.17), although the magnitude is generally larger than found for CMIP5 projections (X. [[#Liu--2020|]] [[#Liu--2020|]] [[#Liu--2020|Liu et al., 2020]] ). There is, however, a strong seasonality in many regions, with a larger relative increase in the winter season of the Northern Hemisphere (NH) and smaller relative changes in the summer (Figure 8.17). Evapotranspiration increases in most land regions, except in areas that are projected to become moisture-limited (due to reduced precipitation and increased evaporative demand), such as the Mediterranean, South Africa, and the Amazonian basin ( ''medium confidence'' ). The patterns of change increase in magnitude from low to high-emissions SSP scenarios ( ''medium co'' ''nfidence'' ).

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[[File:5413af4c229e74f01b1a4990457645c8 IPCC_AR6_WGI_Figure_8_17.png]]

'''Figure 8.17 |''' '''Projected long-term relative changes in seasonal mean evapotranspiration.''' Global maps of projected relative changes (%) in seasonal mean of surface evapotranspiration for December–January–February (DJF; left panels) and June–July–August (JJA; right panels) averaged across available CMIP6 models (number provided at the top right of each panel) for SSP1.2-6 '''(a, b)''' SSP2-4.5 '''(c, d)''' and SSP5-8.5 '''(e, f)''' scenario respectively. All changes are estimated in 2081–2100 relative to 1995–2014. Uncertainty is represented using the simple approach. No overlay indicates regions with high model agreement, where ≥80% of models agree on sign of change; diagonal lines indicate regions with low model agreement, where &lt;80% of models agree on sign of change. For more information on the simple approach, please refer to the Cross-Chapter Box Atlas.1. Further details on data sources and processing are available in the chapter data table (Table 8.SM.1).

In summary, future projections indicate that anthropogenic forcings will drive an increase in global mean evaporation over most oceanic areas ( ''high confidence'' ) (Figure 8.17), an increase in global atmospheric demand ( ''virtually certain'' ) and an increase in evapotranspiration over most land areas, with the exception of moisture-limited regions ( ''medium confidence'' ). However, substantial uncertainties in projections of evapotranspiration, especially at seasonal and regional scales, remain (see also [[#8.2.3.3|Section 8.2.3.3]] and Cross-Chapter Box 5.1).

<div id="8.4.1.5" class="h3-container"></div>

<span id="runoff-streamflow-and-flooding-1"></span>
==== 8.4.1.5 Runoff, Streamflow and Flooding ====

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The AR5 assessed that projected changes in runoff had ''low confidence'' over the period 2016–2035; however, under the RCP8.5 scenario, runoff increases by 2100 are ''likely'' in high northern latitudes. This is consistent with projected regional precipitation increases, based on consistency of changes across different generations of models and different forcing scenarios, and with runoff decreases being ''likely'' in southern Europe, the Middle East and southern Africa. There was considerable uncertainty in the magnitude and direction of change for some regions, largely driven by the uncertainty in projected precipitation changes, particularly across south Asia. For flooding, AR5 assessed with ''medium confidence'' that flooding would increase over parts of South and South East Asia, tropical Africa, north-east Eurasia, and South America, and decrease for parts of Northern and Eastern Europe, Anatolia, Central Asia, Central North America, and southern South America. The SR1.5 assessed with ''medium confidence'' that warming of 2°C would increase the fraction of global area affected by flood hazard relative to warming of 1.5°C. Projected climate-driven changes to runoff, streamflow, and flooding will occur in the context of potential human-caused land-use and land-cover changes, which can have a large influence on surface water ( [[#Sterling--2013|Sterling et al., 2013]] ) but which have considerable uncertainty in projections ( [[#Prestele--2016|Prestele et al., 2016]] ).

Since AR5, studies confirm that global mean annual runoff increases with global surface temperature increase (X. Zhang et al. , 2014, 2018; Lehner et al. , 2019) , but varies regionally (Chen et al. , 2017; H. Yang et al. , 2017; Cook et al. , 2020) . CMIP5 models display a large spread in the ratio of runoff to precipitation for the present-day climate, which applies also to future runoff changes under global warming ( [[#Lehner--2019|Lehner et al., 2019]] ). In studies of CMIP6 projections, runoff increases in most parts of the northern high latitudes and Asia and north and eastern Africa, and decreases in the Mediterranean region, southern Africa, southern Australia and in parts of western Africa, as well as in Central and South America ( [[#Greve--2018|Greve et al., 2018]] ; [[#Cook--2020|Cook et al., 2020]] ). Projected changes in runoff also vary seasonally. In the Northern Hemisphere (NH), runoff increases during winter since more precipitation falls as rain than snow and decreases in the summer as less snow is available to contribute to runoff during the warm season ( [[#Cook--2020|Cook et al., 2020]] ). Global maps of projected changes for December–January–February and June–July–August are shown in Figure 8.18, showing projected changes becoming larger and more consistent in the higher emissions scenarios. Runoff projections for CMIP6 are also shown in Figure 8.16 for tropical and extratropical averages at a range of global mean warming levels and in Table 8.1 for global land in different future scenarios. In the tropics, both the mean and interannual variability of runoff increase with warming. The increase in variability is roughly twice as large as the increase in the mean, and has a large spread across models. In the extratropics, changes are small in the summer but there are large increases in the winter, with the mean increasing much more than the variability, in contrast to the tropics.

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[[File:9188b40f55d99aba09a8dbd80987021e IPCC_AR6_WGI_Figure_8_18.png]]

'''Figure 8.18 |''' '''Projected long-term relative changes in seasonal mean runoff.''' Global maps of projected relative change (%) in runoff seasonal mean for December–January–February (DJF; left panels) and June–July–August (JJA; right panels) averaged across available CMIP6 models (number provided at the top right of each panel) SSP1.2-6 '''(a, b)''' , SSP2-4.5 '''(c, d)''' and SSP5-8.5 '''(e, f)''' scenario respectively. All changes are estimated in 2081–2100 relative to 1995–2014. Uncertainty is represented using the simple approach. No overlay indicates regions with high model agreement, where ≥80% of models agree on sign of change, diagonal lines indicate regions with low model agreement, where &lt;80% of models agree on sign of change. For more information on the simple approach, please refer to the Cross-Chapter Box Atlas.1. Further details on data sources and processing are available in the chapter data table (Table 8.SM.1).

Changes in streamflow vary regionally and increase in magnitude with emissions scenarios, as with runoff (although the two are not equivalent, as runoff includes both surface runoff and streamflow). Streamflow projections additionally require the use of hydrologic models forced by the output from climate models and have not been as widely explored as they are not variables directly included in climate models. On an annual basis, streamflows have been projected to increase in the northern high latitudes and tropical Asia and Africa, and to decrease in the Mediterranean, tropical South America, and South Africa ( [[#Döll--2018|Döll et al., 2018]] ). For a 4°C global warming, half of the global land area is projected to be exposed to increased high flows (average increase 25%), while about 60% may be exposed to decreased low flows (average decrease 50%) ( [[#Asadieh--2017|Asadieh and Krakauer, 2017]] ).

Changes in the seasonality of runoff and streamflow are assessed in Box 8.2. The seasonality of runoff and streamflow (calculated as the annual difference between the wettest and driest months of the year), is expected to increase with global warming in the subtropics, especially in the Mediterranean and southern Africa with ''high confidence'' , and in the Amazon with ''medium confidence'' . For regions where snowmelt is an important contributor to streamflow, there is ''high confidence'' that snowmelt occurring earlier in the year will result in peak flows also occurring earlier in the year, and ''medium confidence'' that reduced snow volume and the weaker solar radiation earlier in the year will reduce the most intense flows (see [[#8.2.3.1|Section 8.2.3.1]] ). In roughly half of 56 large-scale glacierized drainage basins, projected runoff changes show an increase until a maximum is reached, beyond which runoff steadily declines because of limited ice volumes ( [[#Huss--2018|Huss and Hock, 2018]] ).

As future changes in flood events are assessed in Chapters 9, 11 and 12, only a summary is presented here. There are a number of complicating factors for projecting both pluvial (overland) and fluvial (river) flooding that limit confidence in their assessment. In addition to precipitation, flooding also depends on basin and river characteristics such as permeability, antecedent soil moisture, and antecedent flow levels for river flooding, so projections of extreme precipitation and flooding are not always closely linked ( [[#8.2.3.2|Section 8.2.3.2]] ). Possible changes in water resources management and land use add another layer of complexity to future changes. There is ''medium confidence'' in a general increase in pluvial and fluvial flooding, although there are large regional variations, discussed further in Sections 11.5.5, and 12.4. There is ''medium confidence'' in a substantial increase in the frequency of extreme sea level events for coastal regions ( [[IPCC:Wg1:Chapter:Chapter-9#9.6.4.2|Section 9.6.4.2]] ) and the associated coastal flooding is regionally assessed in [[IPCC:Wg1:Chapter:Chapter-12#12.4|Section 12.4]] . The risk of glacier lake outburst floods (GLOFs) is expected to increase with glacier melting in some high mountain regions ( [[IPCC:Wg1:Chapter:Chapter-12#12.4|Section 12.4]] ).

In summary, there is ''medium confidence'' that global runoff will increase with global warming, but with large regional and seasonal variations. There is ''high confidence'' that runoff will increase in the northern high latitudes and decrease in the Mediterranean region and southern Africa. There is ''medium confidence'' that runoff will increase in regions of central and eastern Africa, and decrease in Central America and parts of southern South America, with the magnitude of the change increasing with emissions. There is ''medium confidence'' that the seasonality of runoff and streamflow will increase with global warming in the subtropics. In snow-dominated regions, there is ''high confidence'' that peak flows associated with spring snowmelt will occur earlier in the year and ''medium confidence'' that snowmelt-induced runoff will decrease with reduced snow, except in glacier-fed basins where runoff may increase in the near term. There is ''medium confidence'' that flooding in general will increase, although with considerable variation based on geographic region and flood type. These projected climate-related changes will occur in the context of human-caused land-use and land-cover changes, which may also have a large influence.

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<span id="aridity-and-drought-1"></span>
==== 8.4.1.6 Aridity and Drought ====

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The AR5 concluded that regional to global-scale projections of aridity and drought remained relatively uncertain compared to other aspects of the water cycle. It reported that there is a ''likely'' increase in drought occurrence ( ''medium confidence'' ) by 2100 in regions that are currently drought-prone under the RCP8.5 scenario due to projected decreases in soil moisture. It stated that it is ''likely'' that the most prominent projected decreases in soil moisture would occur in the Mediterranean, south-western USA, and southern Africa, consistent with projected changes in the Hadley circulation and increased surface temperatures. These AR5 conclusions are generally supported by more recent analyses of CMIP5 models ( [[#Feng--2013|Feng and Fu, 2013]] ; [[#Berg--2017|Berg et al., 2017]] ; [[#Cook--2018|Cook et al., 2018]] ).

Results from the latest generation of models in CMIP6 are largely congruent with CMIP5. Consistent with the coherent nature of warming in future projections, increases in vapour pressure deficit and evaporative demand are widespread and consistent across regions, seasons, and models, increasing in magnitude in accordance with the emissions scenario ( ''high confidence'' ) (Figure 8.19; [[#Scheff--2014|Scheff and Frierson, 2014]] , 2015; [[#Vicente-Serrano--2020|Vicente-Serrano et al., 2020]] ). Even under a low-emissions scenario (SSP1-2.6), projections of soil moisture show significant decreases in the Mediterranean, southern Africa, and the Amazonian basin ( ''high confidence'' ) (Figure 8.19). Under mid- and high-emissions scenarios (SSP2-4.5 and SSP5-8.5), coherent declines emerge across Europe, westernmost North Africa, south-western Australia, Central America, south-western North America, and south-western South America ( ''high confidence'' ) (Figure 8.19; [[#Cook--2020|Cook et al., 2020]] ). Compared to CMIP5 results, CMIP6 models exhibit more consistent drying in the Amazonian basin ( [[#Parsons--2020|Parsons, 2020]] ), more extensive declines in total soil moisture in Siberia ( [[#Cook--2020|Cook et al., 2020]] ), and stronger declines in westernmost North Africa and south-western Australia (Figure 8.19).

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[[File:4a50edd8b2c74bd6af406606bf94d850 IPCC_AR6_WGI_Figure_8_19.png]]

'''Figure 8.19 |''' '''Projected long-term relative changes in annual mean soil moisture and vapour pressure deficit.''' Global maps of projected relative changes (%) in annual mean vapor pressure deficit (left), surface soil moisture (top 10cm, middle) and total column soil moisture (right) from available CMIP6 models (number provided at the top right of each panel) for the SSP1.2-6 '''(a, b, c)''' , SSP2-4.5 '''(d, e, f)''' and SSP5-8.5 '''(g, h, i)''' scenarios respectively. All changes are estimated for 2081–2100 relative to a 1995–2014 base period. Uncertainty is represented using the simple approach. No overlay indicates regions with high model agreement (‘Robust change’), where ≥80% of models agree on sign of change, diagonal lines indicate regions with low model agreement, where &lt;80% of models agree on sign of change. For more information on the simple approach, please refer to the Cross-Chapter Box Atlas.1. Further details on data sources and processing are available in the chapter data table (Table 8.SM.1).

Soil moisture in the top soil layer (10 cm) shows more widespread drying than total soil moisture, reflecting a greater sensitivity of the upper soil layer to increasing evaporative demand (Figure 8.19; [[#Berg--2017|Berg et al., 2017]] ). Conversely, total column soil moisture represents the carry-over of moisture from previous seasons deeper in the soil column, and potentially higher sensitivity to vegetation processes ( [[#Berg--2017|Berg et al., 2017]] ; [[#Kumar--2019|Kumar et al., 2019]] ). Central America, the Amazonian basin, the Mediterranean region, southern Africa, and south-western Australia are projected to experience significant declines in total soil moisture, whereas declines in Europe (north of the Mediterranean), western Siberia, and north-eastern North America are limited to the surface (Figure 8.19). It should be noted that because models differ in their number of hydrologically active layers, there is less confidence in total soil moisture projections than surface soil moisture projections. Based on surface soil moisture projections, more than 40% of global land areas (excluding Antarctica and Greenland) are expected to experience robust year-round drying, even under lower emissions scenarios ( [[#Cook--2020|Cook et al., 2020]] ). The percentage of land area experiencing drying is slightly lower when runoff is used as an aridity metric instead (20–30%); taking this into consideration, it is estimated that about a third of global land areas will experience at least moderate drying in response to anthropogenic emissions, even under SSP1-2.6 ( ''medium confidence'' ) ( [[#Cook--2020|Cook et al., 2020]] ).

Although there are regions where multiple models predict consistent and significant changes in soil moisture, as with evapotranspiration ( [[#8.4.1.4|Section 8.4.1.4]] ), there is still uncertainty in these projections related to the response of plants to elevated CO <sub>2</sub> . Most models project increases in two variables that have opposite effects on surface water availability: plant water use efficiency (WUE) and leaf area index (LAI; see [[#8.4.1.4|Section 8.4.1.4]] ). As discussed in Sections 8.2.3.3, 8.3.1.4 and 8.4.1.4, there is ''low confidence'' in how these changes in plant physiology will affect future projections of evapotranspiration, and likewise, drought and aridity.

Changes in meteorological (precipitation-based) drought duration and intensity in CMIP6 models are more robust than projected changes in mean precipitation, more than found in CMIP5 projections ( [[#Ukkola--2020|Ukkola et al., 2020]] ). Significant increases in drought duration are expected in Central America, the Amazonian basin, south-western South America, the Mediterranean, westernmost North Africa, southern Africa, and south-western Australia, on the order of 0.5 to 1 month for a moderate emissions scenario (SSP2-4.5) and two months for a high-emissions scenario (SSP5-8.5; [[#Ukkola--2020|Ukkola et al., 2020]] ). Drought intensity is projected to increase in the tropics, mainly in the Amazonian basin, Central Africa, and southern Asia, as well as in Central America and south-western South America ( [[#Ukkola--2020|Ukkola et al., 2020]] ). The CORDEX South Asia multi-model ensemble projections indicate an increase in the frequency and severity of droughts over central and northern India during the 21st century, under the RCP4.5 and RCP8.5 scenarios ( ''medium confidence'' ) ( [[#Mujumdar--2020|Mujumdar et al., 2020]] ). Under intermediate or high-emissions scenarios, the likelihood of extreme droughts (events that have magnitudes equal to or less than the 10th percentile of the 1851–1880 baseline period) increases by 200–300% in the Amazonian basin, south-western North America, Central America, the Mediterranean, southern Africa, and south-western South America ( [[#Cook--2020|Cook et al., 2020]] ). Even under a low-emissions scenario (SSP1-2.6), the likelihood of extreme droughts increases by 100% in south-western North America, south-western South America, the Amazon, the Mediterranean, and southern Africa ( [[#Cook--2020|Cook et al., 2020]] ). Thus, there is ''high confidence'' that drought severity and intensity will increase in the Mediterranean, southern Africa, south-western South America, south-western North America, south-western Australia, Central America and the Amazonian basin.

Paleoclimate records provide context for these future expected changes in drought and aridity. In the Mediterranean, western North America, and Central Chile, there is ''high confidence'' that climate change will shift soil moisture (as represented by the Palmer Drought Severity Index) outside the range of observed and reconstructed values spanning the last millennium (Figure 8.20; [[#Cook--2014|Cook et al., 2014]] ; [[#Otto-Bliesner--2016|Otto-Bliesner et al., 2016]] ). Warmer temperatures, leading to increased evaporative losses, are clearly implicated in the projected future drying in these semi-arid regions ( [[#Dai--2018|Dai et al., 2018]] ), emphasizing the central role that warming plays in driving increased evaporative demand ( [[#Vicente-Serrano--2020|Vicente-Serrano et al., 2020]] ). In contrast, future trajectories are more uncertain in regions like Central Asia and eastern Australia–New Zealand where projected changes in precipitation and soil moisture are less coherent (Figure 8.19 and 8.20; [[#Hessl--2018|Hessl et al., 2018]] ). More information on projected changes in drought, including specific categories or drought, can be found in [[IPCC:Wg1:Chapter:Chapter-11#11.6.5|Section 11.6.5]] and [[IPCC:Wg1:Chapter:Chapter-12#12.4|Section 12.4]] .

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[[File:9a77c8b4542932f9c263c3a08a9cc58c IPCC_AR6_WGI_Figure_8_20.png]]

'''Figure 8.20 |''' '''Past-to-future drought variability in paleoclimate reconstructions and models for select regions.''' On the left '''(a, c, e, g, i)''' , tree-ring reconstructed Palmer Drought Severity Index (PDSI) series (black line) for the Mediterranean (10°W–45°E, 30°–47°N; E.R. [[#Cook--2015|]] [[#Cook--2015|]] [[#Cook--2015|Cook et al., 2015]] ; [[#Cook--2016a|Cook et al., 2016a]] ), central Chile (70°W–74°W, 32°S–37°S; [[#Morales--2020|Morales et al., 2020]] ), western North America (117°W–124°W, 32°N–38°N; [[#Cook--2010|Cook et al., 2010]] ; [[#Griffin--2014|Griffin and Anchukaitis, 2014]] ), Eastern Australia and New Zealand (136°E–178°E, 46°S–11°S; [[#Palmer--2015|Palmer et al., 2015]] ), and Central Asia (99°E–107°E, 47°N–49°N; [[#Pederson--2014|Pederson et al., 2014]] ; [[#Hessl--2018|Hessl et al., 2018]] ) plotted in comparison to the past-to-future, fully-forced simulations from four ensemble members (thin blue lines) from the NCAR CESM Last Millennium Ensemble (thick blue line = ensemble mean) ( [[#Otto-Bliesner--2016|Otto-Bliesner et al., 2016]] ) for the same regions. The shaded area represents the range (10th to 90th percentile) of historical and future (RCP8.5) PDSI (Penman–Monteith) simulations from 15 CMIP5 models and 34 ensemble members for the same regions (1900–2100; [[#Cook--2014|Cook et al., 2014]] ). On the right '''(b, d, f, h, j)''' , the distribution of annual PDSI values from the past and present (850 to 2005 CE) (black) is compared to the future distribution (2006 to 2100 CE) (blue). The distributions show each of the four ensemble members from the CESM LME simulations. The future component of the CESM LME follows the RCP8.5 scenario. Further details on data sources and processing are available in the chapter data table (Table 8.SM.1).

In summary, there is ''high confidence'' that soil moisture will decline in semi-arid, winter-rainfall dominated areas including the Mediterranean, southern Africa, south-western North America, south-western South America, and south-western Australia, as well as in Central America and the Amazonian basin. In general, these regions are expected to become drier both due to reduced precipitation ( ''medium confidence'' ) and increases in evaporative demand ( ''high confidence'' ). These same regions are ''likely'' to experience increases in drought duration and/or severity ( ''high confidence'' ). The magnitude of expected change scales with emissions scenarios ( ''high confidence'' ) but even under low-emissions trajectories, large changes in drought and aridity are expected to occur ( ''high confidence'' ) with consequences for regional water availability. In the Mediterranean, Central Chile, and western North America, future aridification will far exceed the magnitude of change seen over the last millennium ( ''high co'' ''nfidence'' ).

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<span id="freshwater-reservoirs-1"></span>
==== 8.4.1.7 Freshwater Reservoirs ====

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<div id="8.4.1.7.1" class="h4-container"></div>

<span id="glaciers-1"></span>
===== 8.4.1.7.1 Glaciers =====

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Previous assessments have concluded that recent warming has led to a reduction in low-elevation snow cover ( ''high confidence'' ) (SROCC), permafrost ( ''high confidence'' ) (SROCC), and glacier mass ( ''high to very high confidence'' ) (AR5; SROCC). The SROCC noted that these declines are projected to continue almost everywhere over the 21st century ( ''high confidence'' ), with complete glacier loss expected in regions with only small glaciers ( ''very high confidence'' ). The SROCC supported the AR5 finding that glacier recession would continue even without further changes in climate. The SROCC concluded that cryosphere changes had already altered the seasonal timing and volume of runoff ( ''very high confidence'' ), which in turn had affected water resources and agriculture ( ''medium confidence'' ), and projected peak water runoff had already been reached before 2019 in some of the glacier regions considered.

( [[IPCC:Wg1:Chapter:Chapter-9|Chapter 9]] provides detailed assessment of glacier observations and projections (Figures 9.20 and 9.21, and [[IPCC:Wg1:Chapter:Chapter-9#9.5.1|Section 9.5.1]] ). Here, a summary of their key findings is presented. Since SROCC, the coordinated Glacier Model Intercomparison Project (GlacierMIP; Box 9.3; [[#Marzeion--2020|Marzeion et al., 2020]] ) has advanced modelling efforts. Global glacier volumes will substantially decline in coming decades regardless of emissions scenario; under a high-emissions scenario some areas will lose nearly all of their glacier mass ( [[IPCC:Wg1:Chapter:Chapter-9#9.5.1.3|Section 9.5.1.3]] ). The projected global glacier mass loss over 2015 – 2100 is 29,000 ± 20,000 Gt for SSP1-2.6 to 58,000 ± 30,000 Gt for SSP5-8.5 ( [[IPCC:Wg1:Chapter:Chapter-9#9.5.1|Section 9.5.1]] ). Because of their lagged response to warming, glaciers will continue to lose mass for decades even if global temperature is stabilized ( ''very high confidence'' ) ( [[IPCC:Wg1:Chapter:Chapter-9#9.5.1|Section 9.5.1]] ).

Global glacier mass loss projections show a scenario-dependent geographic partitioning of when peak in runoff occurs ( [[#Marzeion--2020|Marzeion et al., 2020]] ), consistent with previous studies ( [[#Radić--2014|Radić et al., 2014]] ; [[#Huss--2018|Huss and Hock, 2018]] ; [[#Hock--2019b|Hock et al., 2019b]] ). Under a low-emissions scenario ( [[#Marzeion--2020|Marzeion et al., 2020]] ) all regions exhibit runoff in the decades prior to 2050. Under a high-emissions scenario however, low- and mid-latitude regions show peak runoff before approximately 2060, whereas Arctic regions peak in later decades around 2070 – 2090. Antarctic glacier losses will not have peaked by the end of the century in the high-emissions scenario. Globally, peak runoff of 2.5 to 3 mm yr <sup>–1</sup> sea level equivalent occurs around 2090 ( [[#Marzeion--2020|Marzeion et al., 2020]] ). Regional projections are presented in detail in [[IPCC:Wg1:Chapter:Chapter-9#9.5.1%20|Section 9.5.1]] and Figure 9.21, and briefly summarized below.

'''Himalaya and Central Asia:''' Glaciers in the Himalayas feed ten of the world’s most important river systems and are critical water sources for nearly two billion people ( [[#Wester--2019|Wester et al., 2019]] ). However, they are some of the most vulnerable ‘water towers’ ( [[#Immerzeel--2020|Immerzeel et al., 2020]] ) that are projected to experience volume losses of approximately 30 to 100% by 2100 depending on global emissions scenarios ( [[#Marzeion--2020|Marzeion et al., 2020]] ). Under mid-range emissions scenarios glaciers in this region are projected to reach peak runoff during the period 2020 to 2040 ( [[#Marzeion--2020|Marzeion et al., 2020]] ).

'''Alaska, Yukon, British Columbia:''' Post-AR5 but pre-SROCC projections indicated a potential 70 ± 10% reduced volume of glacier ice in western Canada relative to 2005 (Clarke et al. , 2015) , with few glaciers remaining in the Interior and Rockies regions and maritime glaciers in north-western British Columbia surviving only in a diminished state. Recent global projections support these earlier findings, showing that glacier mass in western Canada and the USA may reduce by 50% under low-emissions scenarios and be completely lost under the highest emissions and most sensitive glacier model combinations (Figure 9.21; Marzeion et al. , 2020) . Arctic Canada and Alaskan glaciers are projected to experience more modest mass loss (0–60% depending on region, scenario, and model; Marzeion et al., 2020) .

'''Andes:''' [[#Huss--2018|Huss and Hock (2018)]] concluded that peak glacier mass was reached prior to 2019 for 82–95% of the glacier area in the tropical Andes. This is consistent with more recent global model simulations that show mass loss rates from low latitude glaciers that universally decline from the start of simulations in 2015, regardless of emissions scenario ( [[#Marzeion--2020|Marzeion et al., 2020]] ). Peak runoff in low-latitude Andean glacier-fed rivers has therefore already passed ( [[#Frans--2015|Frans et al., 2015]] ; [[#Polk--2017|Polk et al., 2017]] ) but in the Southern Andes may occur in the latter half of the century under high-emissions scenarios ( [[#Marzeion--2020|Marzeion et al., 2020]] ).

In summary, glaciers are projected to continue to lose mass under all emissions scenarios ( ''very high confidence'' ). Runoff from glaciers is projected to peak at different times in different places, with maximum rates of glacier mass loss in low latitude regions taking place in the next few decades in all scenarios ( ''high confidence'' ). While runoff from small glaciers will typically decrease because of glacier mass depletion, runoff from larger glaciers will increase with increasing global warming until glacier mass is similarly depleted, after which runoff peaks and then declines and which tends to occurs later in basins with larger glaciers and higher ice-cover fractions ( ''high confidence'' ). Glaciers in the Arctic and Antarctic will continue to lose mass through the latter half of the century and beyond ( ''high co'' ''nfidence'' ).

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<span id="seasonal-snow-cover-1"></span>
===== 8.4.1.7.2 Seasonal snow cover =====

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The AR5 assessed as ''very likely'' that the amount and seasonal duration of Northern Hemisphere (NH) snow cover will reduce under global warming (AR5 Sections 11.3.4.2 and 12.4.6.2). Changes in the total amount of water in the snow cover (snow water equivalent) are less certain because of the competing influences of temperature and precipitation.

As snow cover is assessed in [[IPCC:Wg1:Chapter:Chapter-9|Chapter 9]] ( [[IPCC:Wg1:Chapter:Chapter-9#9.5.3.3|Section 9.5.3.3]] ), only an overview of that assessment is provided here. Changes in seasonality of snow cover are assessed in Box 8.2. The continued consistency of reported results across all generations of model projections, along with improvements in process understanding, has increased confidence in snow cover projections since AR5.

In summary, based on the results of Chapter 9, it is now ''virtually certain'' that future NH snow cover extent and duration will continue to decrease with global warming. While most studies have focused on the NH, process understanding suggests with ''high confidence'' that these results apply to the Southern Hemisphere (SH) as well. There is ''high confidence'' in snowmelt occurring earlier in the year. Changes to the timing and amount of snowmelt will have a strong influence on all the other aspects of the water cycle in regions with seasonal snow, including run-off, soil moisture, and evapotranspiration.

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<span id="wetlands-and-lakes-1"></span>
===== 8.4.1.7.3 Wetlands and lakes =====

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The AR5 did not include specific projections for wetlands and lakes. The SRCCL and SROCC provided some discussion of wetlands projections. For coastal wetlands, SRCCL noted the importance of sea level rise for increased saltwater intrusion, although projections of coastal wetland area with sea level rise are inconclusive. Some studies project substantial decreases ( [[#Spencer--2016|Spencer et al., 2016]] ) while others indicate possible increases ( [[#Schuerch--2018|Schuerch et al., 2018]] ). SRCCL also noted the general expectation for decreases in water resources, including wetlands, in areas of decreased rainfall due to increased evaporation.

Local studies of inland wetlands project decreases in a range of environments including mountain ( [[#Lee--2015|Lee et al., 2015]] ), mid- to high latitude (D. [[#Zhao--2018|]] [[#Zhao--2018|]] [[#Zhao--2018|Zhao et al., 2018]] ), and prairie (Sofaer et al. , 2016) regions. In addition to affecting wetland extent and density, changes in flooding can also affect the connectivity between wetlands and rivers (Karim et al. , 2016). Despite a number of uncertainties underlying the general response of wetlands to climate change, there are multiple ways climate change may cause considerable stress on both inland and coastal wetlands (Junk et al. , 2013; Moomaw et al. , 2018).

Widespread changes are also projected for lakes ( [[#Woolway--2020|Woolway et al., 2020]] ), including changes in lake temperature ( [[#Fang--1999|Fang and Stefan, 1999]] ; [[#Sahoo--2016|Sahoo et al., 2016]] ), ice ( [[#Sharma--2019|Sharma et al., 2019]] ), evaporation (W. [[#Wang--2018|]] [[#Wang--2018|]] [[#Wang--2018|]] [[#Wang--2018|]] [[#Wang--2018|]] [[#Wang--2018|Wang et al., 2018]] ), and stability and mixing ( [[#Woolway--2019|Woolway and Merchant, 2019]] ). Note that lake ice is also considered in [[IPCC:Wg1:Chapter:Chapter-12|Chapter 12]] of this Report. To date, CO <sub>2</sub> -induced lake acidification, analogous to ocean acidification, has not been the focus of many studies but may occur with continued emissions ( [[#Phillips--2015|Phillips et al., 2015]] ). While glacier lakes in general increase with melting glaciers ( [[#Linsbauer--2016|Linsbauer et al., 2016]] ; [[#Colonia--2017|Colonia et al., 2017]] ; [[#Magnin--2020|Magnin et al., 2020]] ) no clear projections are currently available (see discussion in Chapter 9). Projections of lake level means and variability show substantial changes for individual lakes ( [[#Bucak--2017|Bucak et al., 2017]] ; [[#Li--2021|Li et al., 2021]] ) but can be sensitive to methodology, due to the competing processes involved ( [[#Notaro--2015|Notaro et al., 2015]] ). Projected changes to wetlands and lakes due to climate change will occur in the context of widespread and continuing human-caused conversion and degradation of wetlands (e.g, [[#Davidson--2014|Davidson, 2014]] ), and where water withdrawals have a large impact on lake levels (e.g., [[#Micklin--2016|Micklin, 2016]] ).

In summary, there is ''medium confidence'' that inland wetland extent will decrease in regions of projected precipitation decrease and evaporation increase, and ''high confidence'' that sea level rise will increase saltwater intrusion into coastal wetlands. However, there is ''low agreement'' on the influence of sea level rise on the extent of coastal wetlands. Regarding lakes, there is ''high confidence'' for temperature increases and ice decreases, based on both projections and physical expectations, and ''low confidence'' for non-homogeneous decreases in mixing, given there is currently ''limited'' ''evidence'' .

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<span id="groundwater-1"></span>
===== 8.4.1.7.4 Groundwater =====

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Groundwater projections were not assessed in AR5. Groundwater processes are not explicitly included in most current CMIP6 models and so must be calculated separately with hydrologic models (e.g., R.G. Taylor et al. , 2013; Cuthbert et al. , 2019a) . A range of factors are important in assessing groundwater projections, including the mean difference between precipitation and evaporation, the intensity of precipitation (R.G. [[#Taylor--2013|Taylor et al., 2013]] a), and in changes in snow ( [[#Tague--2009|Tague and Grant, 2009]] ), glaciers ( [[#Gremaud--2009|Gremaud et al., 2009]] ), and permafrost ( [[#Okkonen--2011|Okkonen and Kløve, 2011]] ). Climate impacts on groundwater are occurring in the context of severe and growing human-caused groundwater depletion (WGII; [[#Konikow--2005|Konikow and Kendy, 2005]] ; [[#Rodell--2018|Rodell et al., 2018]] ; [[#Bierkens--2019|Bierkens and Wada, 2019]] ), and water scarcity issues ( [[#Mekonnen--2016|Mekonnen and Hoekstra, 2016]] ). Climate-related changes to the water cycle can influence water demand (for example, precipitation decreases in an irrigated area), and anthropogenic groundwater depletion can influence the water cycle through interactions with surface energy fluxes, surface water, and vegetation ( [[#Cuthbert--2019a|Cuthbert et al., 2019a]] ), although uncertainties in estimates of future groundwater depletion are large ( [[#Smerdon--2017|Smerdon, 2017]] ; [[#Bierkens--2019|Bierkens and Wada, 2019]] ) . Some aspects of groundwater change will be irreversible, including the increase of saltwater intrusion into coastal aquifers with sea level rise ( [[#Werner--2009|Werner and Simmons, 2009]] ), and depletion of fossil aquifers and aquifers with very long recharge times ( [[#Bierkens--2019|Bierkens and Wada, 2019]] ).

Globally, two modelling studies have shown substantial decreases in groundwater in regions including the Mediterranean, north-eastern Brazil and south-western Africa, with less clarity for other regions ( [[#Döll--2009|Döll, 2009]] ; Portmann et al. , 2013) . Recent regional-scale analyses of the impact of water cycle changes on groundwater recharge (e.g., Meixner et al. , 2016; Tillman et al. , 2017; Shrestha et al. , 2018 ) suggest changes in both seasonality and spatial distribution, which are amplified under a higher greenhouse-gas emissions scenario (i.e., RCP 8.5 compared to RCP4.5). Seasonality changes are linked to increases during wet winter periods and declines during dry summer periods. Changes in spatial distribution are linked with increases in more humid regions and declines in more arid locations. Uncertainty in projections of groundwater were found to be substantially influenced by the conceptual and numerical models employed to estimate groundwater recharge ( [[#Meixner--2016|Meixner et al., 2016]] ; [[#Hartmann--2017|Hartmann et al., 2017]] ). Accordingly, current research on estimating water cycles change on groundwater includes a focus on improving the numerical representation of groundwater systems ( [[#Bierkens--2015|Bierkens et al., 2015]] ; [[#Döll--2016|Döll et al., 2016]] ).

In summary, based on known limitations in current modelling, no confident assessment of groundwater projections is made here, although important climate-related changes in groundwater recharge are expected. In many environments, such climate-related impacts are expected to occur in the context of substantial human groundwater withdrawals depleting groundwater storage.

<div id="8.4.2" class="h2-container"></div>

<span id="projected-changes-in-large-scale-phenomena-and-regional-variability"></span>
=== 8.4.2 Projected Changes in Large-scale Phenomena and Regional Variability ===

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A weakening of the tropical circulation represents a balance between thermodynamic increases in low level water vapour (about 7% °C <sup>–1</sup> ) and smaller increases in global precipitation (1 – 3% °C <sup>–1</sup> ) that are influenced by rapid adjustments to radiative forcings as well as slow responses to warming ( [[#8.2.2.2|Section 8.2.2.2]] ; Bony et al. , 2013; Chadwick et al. , 2013; Ma et al. , 2018) . Since AR5, additional drivers of tropical circulation weakening have been identified, including mean SST warming and changes in spatial patterns of SST ( [[#He--2015|He and Soden, 2015]] ), and the direct CO <sub>2</sub> radiative effect ( [[#Bony--2013|Bony et al., 2013]] ; [[#He--2015|He and Soden, 2015]] ; [[#Merlis--2015|Merlis, 2015]] ).

<div id="8.4.2.1" class="h3-container"></div>

<span id="itcz-and-tropical-rain-belts"></span>
==== 8.4.2.1 ITCZ and Tropical Rain Belts ====

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CMIP5 projections show no consistent shift in the zonal mean position of the ITCZ ( Donohoe et al. , 2013; [[#Donohoe--2017|Donohoe and Voigt, 2017]] ; Byrne et al. , 2018 ). The ITCZ position is strongly connected to cross-equatorial energy transport ( Kang et al. , 2008; [[#Bischoff--2014|Bischoff and Schneider, 2014]] ), which also shows no consistent change in future projections ( [[#Donohoe--2013|Donohoe et al., 2013]] ). Since AR5 it has been reported that most CMIP5 models project a narrowing of the ITCZ in response to surface warming together with intensified ascent in the core region and weakened ascent on the ITCZ edges ( [[#Lau--2015|Lau and Kim, 2015]] ; [[#Byrne--2018|Byrne et al., 2018]] ), implying a narrowing of precipitation regions influenced by the ITCZ. Modelled changes in the width and intensity of the zonal mean ITCZ are strongly anti-correlated, for example, narrowing is associated with increased intensity while broadening with decreased intensity. Such changes are associated with changes in tropical high cloud fraction and outgoing longwave radiation ( [[#Su--2017|Su et al., 2017]] ; [[#Byrne--2018|Byrne et al., 2018]] ).

Regional shifts in tropical convergence zones are much larger than their zonal mean, and associated regional changes in precipitation ( [[#Chadwick--2013|Chadwick et al., 2013]] ; [[#Mamalakis--2021|Mamalakis et al., 2021]] ) are characterized by considerable uncertainties across models ( [[#Kent--2015|Kent et al., 2015]] ; [[#Oueslati--2016|Oueslati et al., 2016]] ). Over the tropical oceans, shifts in rain bands are strongly coupled with changes in SSTs (Xie et al. , 2010; Huang et al. , 2013) . Over tropical land, factors including remote SST increases ( [[#Giannini--2010|Giannini, 2010]] ), the direct CO <sub>2</sub> effect ( [[#Biasutti--2013|Biasutti, 2013]] ) and land–atmosphere interactions ( [[#Chadwick--2017|Chadwick et al., 2017]] ; [[#Kooperman--2018|Kooperman et al., 2018]] ) influence projections. CMIP6 models project a clear northward ITCZ shift over eastern Africa and the Indian Ocean as well as a southward shift over the eastern Pacific and Atlantic oceans, as a result of regionally-contrasting inter-hemispheric energy flows ( [[#Mamalakis--2021|Mamalakis et al., 2021]] ). The northward movement of the ITCZ over Africa has been linked to an intensification of the Saharan heat low associated with greenhouse gas (GHG) warming ( [[#Dong--2015|Dong and Sutton, 2015]] ), causing the tropical rain belt to seasonally migrate farther northward and reside there longer ( [[#Cook--2012|Cook and Vizy, 2012]] ; [[#Dunning--2018|Dunning et al., 2018]] ). In southern Africa, the projected delay in the wet season onset (Dunning et al. , 2018) is also associated with a circulation-based northward shift in the tropical rain band (Lazenby et al., 2018) .

In summary, consistent with the AR5, the overall weakening of the tropical circulation is projected in CMIP5 and CMIP6 simulations with ''high confidence'' . It is ''likely'' that the zonal mean of the ITCZ will narrow and strengthen in the core region with projected surface warming ( ''high confidence'' ). Distinct regional shifts in the ITCZ will be associated with regional changes in precipitation amount and seasonality ( ''medium co'' ''nfidence'' ).

<div id="8.4.2.2" class="h3-container"></div>

<span id="hadley-circulation-and-subtropical-belt-1"></span>
==== 8.4.2.2 Hadley Circulation and Subtropical Belt ====

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The AR5 found that the Hadley cells are ''likely'' to slow down and expand in response to radiative forcing, but with considerable internal variability. Given the complexities in forcing mechanisms, AR5 assigned ''low confidence'' to near-term changes in the structure of the Hadley circulation. The widening Hadley cells were expected to result in a poleward expansion of subtropical dry zones.

Model simulations since AR5 project a more noticeable and consistent weakening of the Northern Hemisphere (NH) winter Hadley cell than the Southern Hemisphere (SH) winter cell ( [[#Seo--2014|Seo et al., 2014]] ; [[#Zhou--2016|Zhou et al., 2016]] ), related to changes in meridional temperature gradient, static stability, and tropopause height ( [[#Seo--2014|Seo et al., 2014]] ; [[#D’Agostino--2017|D’Agostino et al., 2017]] ). Changes in SST patterns reduces the magnitude of Hadley cell weakening ( [[#Gastineau--2009|Gastineau et al., 2009]] ; [[#Ma--2012|Ma et al., 2012]] ). There is considerable structure in Hadley circulation strength changes with longitude, associated with cloud-circulation interactions ( [[#Su--2014|Su et al., 2014]] ). Subtropical anticyclones are projected to intensify over the north Atlantic and south Pacific but to weaken elsewhere ( [[#He--2017|He et al., 2017]] ).

A consistent poleward expansion of the edges of the Hadley cells is projected ( [[#Nguyen--2015|Nguyen et al., 2015]] ; [[#Grise--2020|Grise and Davis, 2020]] ), particularly in the SH, consistent with observed trends (Figure 8.21 and [[#8.3.2.2|Section 8.3.2.2]] ; Nguyen et al. , 2015) . The main driver of future expansion appears to be greenhouse gas forcing ( [[#Grise--2019|Grise et al., 2019]] ), with uncertainty in magnitude due to internal variability ( [[#Kang--2013|Kang et al., 2013]] ). Proposed mechanisms for poleward expansion include increased dry static stability (Frierson et al. , 2007; Lu et al. , 2007) , increased tropopause height ( [[#Chen--2007|Chen and Held, 2007]] ; Chen et al. , 2008) , stratospheric influences ( [[#Kidston--2015|Kidston et al., 2015]] ) and radiative effects of clouds and water vapour ( [[#Shaw--2016|Shaw and Voigt, 2016]] ; see also [[IPCC:Wg1:Chapter:Chapter-4#4.5.1.5|Section 4.5.1.5]] ). Hadley cell expansion is thought to be associated with the precipitation declines projected in many subtropical regions ( [[#Shaw--2016|Shaw and Voigt, 2016]] ), but more recent work suggests that these reductions are mainly due to the direct radiative effect of CO <sub>2</sub> forcing ( [[#He--2015|He and Soden, 2015]] ), land – sea contrasts in the response to forcing (Shaw and Voigt, 2016; Brogli et al. , 2019) and SST changes ( [[#Sniderman--2019|Sniderman et al., 2019]] ). In semi-arid, winter rainfall-dominated regions (such as the Mediterranean), thermodynamic processes associated with the land – sea thermal contrast and lapse rate changes dominate the projected precipitation decline in summer, whereas circulation changes are of greater importance in winter (Brogli et al. , 2019) . The hydroclimates in these regions are projected to evolve with time due to changing contributions from rapid atmospheric circulation changes and their associated SST responses, as well as slower SST responses to anthropogenic forcing (Zappa et al., 2020) .

In summary, CMIP5 and CMIP6 models project a weakening of the Hadley cells, with ''high confidence'' for the NH in boreal winter and ''low confidence'' for the SH in austral winter. The Hadley cells are projected to expand polewards with global warming, most notably in the SH ( ''high confidence'' ). There is currently ''low confidence'' in the impacts on regional precipitation in subtropical regions.

<div id="8.4.2.3" class="h3-container"></div>

<span id="walker-circulation-1"></span>
==== 8.4.2.3 Walker Circulation ====

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The AR5 determined that the Pacific Walker circulation was ''likely'' to slow down over the 21st century, which would lead to decreased precipitation over the western tropical Pacific and increases over the central and eastern Pacific. Recent studies show consistency with AR5 conclusions but also show an eastward shift over the Pacific, mostly due to a shift towards more ‘El Niño-like’ conditions under global warming ( [[#Bayr--2014|Bayr et al., 2014]] ). Other studies suggest that the weakening of the Walker circulation is related to the response of the western North Pacific monsoon and to changing land–sea temperature contrasts, while a positive ocean–atmosphere feedback amplifies the weakening of both east–west SST gradient and trade winds in the tropical Pacific (Zhang and Li, 2017).

Since AR5, the paradox between the projected weakening and the observed strengthening of the Walker circulation since the 1990s ( [[#8.3.2.2|Section 8.3.2.2]] ) has triggered debate about the drivers of these changes (England et al. , 2014; McGregor et al. , 2014; [[#Kociuba--2015|Kociuba and Power, 2015]] ; Vilasa et al. , 2017; Chung et al. , 2019) . Projected changes in equatorial SST gradients are not entirely consistent with observed trends (Coats and Karnauskas, 2017; [[#Seager--2019a|Seager et al., 2019a]] ), and one CMIP5 model that projects a future strengthening of the Walker circulation is more consistent with observations than other models (Kohyama et al., 2017). Other studies suggest that these differences arise from the dominant influence of internal climate variability to the observed trends ( [[#Chung--2019|Chung et al., 2019]] ), or as a consequence of a systematic cold bias of most CMIP5 models in their Equatorial Pacific cold tongues ( [[#Seager--2019a|Seager et al., 2019a]] ). However, the latter hypothesis is based on a simplified model of tropical Pacific dynamics and is not consistent with the current physical understanding of the tropical circulation response to increasing CO <sub>2</sub> levels ( [[#8.2.2.2|Section 8.2.2.2]] ) or with independent paleoclimate evidence suggesting a weaker Walker circulation under warmer climates ( [[#Tierney--2019|Tierney et al., 2019]] ; [[#McClymont--2020|McClymont et al., 2020]] ). Different time scales of the tropical Pacific responses to global warming have been highlighted by numerical experiments with both comprehensive and simplified models. Results suggest a transient strengthening of the Walker circulation related to Indian Ocean warming (L. [[#Zhang--2018|]] [[#Zhang--2018|]] [[#Zhang--2018|]] [[#Zhang--2018|]] [[#Zhang--2018|Zhang et al., 2018]] ), followed by a slower weakening linked to a strengthened eastern Pacific cold tongue warming emerging after 50 – 100 years ( [[IPCC:Wg1:Chapter:Chapter-7#7.4.4.2.1|Section 7.4.4.2.1]] ; [[#Heede--2020|Heede et al., 2020]] ).

CMIP6 projections provide further evidence of a significant long-term weakening of the Walker circulation (Figure 8.21). For instance, a pronounced weakening of the upper-level tropical easterly jet is projected both over the Indian Ocean and tropical eastern Pacific, where declines are projected to exceed 70% by 2100 in the high-emissions SSP5-8.5 scenario (S. [[#Huang--2020|Huang et al., 2020]] ). CMIP6 models agree on a future decrease of the equatorial zonal temperature gradient ( [[#Fredriksen--2020|Fredriksen et al., 2020]] ), which can lead to weaker trade winds over the tropical Pacific. However, CMIP6 models show a diversity of SST warming patterns in the tropical Pacific ( [[#Freund--2020|Freund et al., 2020]] ), which contributes to uncertainties in the response of both Walker circulation and ENSO to continued warming.

<div id="_idContainer061" class="•-Graphic-insert"></div>

[[File:1db82c46f3cd13224b03acaa27e2a877 IPCC_AR6_WGI_Figure_8_21.png]]

'''Figure 8.21 |''' '''Schematic depicting large-scale circulation changes and impacts on the regional water cycle.''' The central figures show precipitation minus evaporation (P–E) changes at 3°C or global warming relative to an 1850–1900 base period (mean of 23 CMIP6 SSP5-8.5 simulations). Annual mean changes (large map) include contours (ocean only) depicting control climate P–E = 0 mm day – 1 lines with the solid contour enclosing the tropical rain belt region and dashed lines representing the edges of subtropical regions. Confidence levels assess understanding of how large-scale circulation change affect the regional water.

In summary, there is ''high confidence'' that the Pacific Walker circulation will weaken by the end of the 21st century, and will be associated with decreased precipitation over the western tropical Pacific and increases farther east. Discrepancies between observed and simulated changes in SSTs in the tropics indicate that a temporary strengthening of the Walker Circulation can arise from a transient response to GHG radiative forcing ( ''low confidence'' ) and from internal variability ( ''medium co'' ''nfidence'' ).

<div id="8.4.2.4" class="h3-container"></div>

<span id="monsoons-1"></span>
==== 8.4.2.4 Monsoons ====

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In AR5, monsoon precipitation over land was projected to intensify by the end of the 21st century, due to thermodynamic increases in moisture convergence despite weakening of the tropical circulation (see [[#8.2.1.3|Section 8.2.1.3]] ). Following the definition of regional monsoons in [[IPCC:Wg1:Chapter:Annex-v|Annex V]] and Figure 8.11, and the assessment of the observed changes ( [[#8.3.2.4|Section 8.3.2.4]] ), here we provide an assessment of projected changes in regional monsoons. Assessment is provided either in terms of SSP and RCP scenarios and global warming levels available since AR5, or from the newly available CMIP6 projections (Figure 8.22 and Table 8.2). Table 8.2 provides projected changes across the five SSPs used in this Report for precipitation (mm day <sup>–1</sup> ), P–E (mm day <sup>–1</sup> ) and runoff (mm day <sup>–1</sup> ) over the regional monsoons for the mid (2041 – 2060) and long term (2081 – 2100).

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'''Table 8.2 |''' '''Monsoon mean water cycle projections in the mid-term''' ( '''2041–2060''' ''') and long term''' ( '''2081–2100''' ''') relative to present day''' ( '''1995–2014''' '''), showing present-day mean and 90% confidence range across CMIP6 models (historical experiment) and projected mean changes and the 90% confidence range across the same set of models and a range of Shared Socio-economic Pathway scenarios. All statistics are in units of mm day''' –1 '''.''' Further details on data sources and processing are available in the chapter data table (Table 8.SM.1).

{| class="wikitable"
|-
!
! colspan="5"| '''Mid-term: 2041–2061 Minus Reference Period'''

! colspan="5"| '''Long Term: 2081–2100 Minus Reference Period'''

|-
!
! '''1995''' – '''2014 Reference Period'''

! '''SSP1-1.9'''

! '''SSP1-2.6'''

! '''SSP2-4.5'''

! '''SSP3-7.0'''

! '''SSP5-8.5'''

! '''SSP1-1.9'''

! '''SSP1-2.6'''

! '''SSP2-4.5'''

! '''SSP3-7.0'''

! '''SSP5-8.5'''

|-
| colspan="12"| '''South and South East Asian Monsoon (June–July–August–September, JJAS)'''

|-
| Precipitation

| 8.42 [6.66 to 10.14]

| 0.44 [0.08 to 0.74]

| 0.47 [0.1 to 0.96]

| 0.42 [0.03 to 0.81]

| 0.32 [–0.08 to +0.94]

| 0.54 [0.11–1.18]

| 0.46 [0.16 to 0.7]

| 0.52 [0.13 to 1.09]

| 0.66 [0.16 to 1.1]

| 0.94 [0.3 to 1.78]

| 1.46 [0.66 to 2.49]

|-
| Runoff

| 3.75 [1.8 to 5.71]

| 0.23 [0.1 to 0.38]

| 0.29 [0.02 to 0.65]

| 0.29 [–0.0 to +0.66]

| 0.24 [–0.04 to +0.52]

| 0.38 [0.07–0.78]

| 0.19 [–0.02 to +0.35]

| 0.29 [–0.04 to +0.65]

| 0.42 [0.04 to 0.83]

| 0.7 [0.12 to 1.2]

| 1.14 [0.36 to 2.05]

|-
| P–E

| 5.19 [3.68 to 6.5]

| 0.28 [0.03 to 0.52]

| 0.36 [–0.0 to +0.76]

| 0.36 [0.02 to 0.69]

| 0.3 [–0.04 to +0.85]

| 0.45 [0.06–0.95]

| 0.27 [0.06 to 0.38]

| 0.38 [0.11 to 0.76]

| 0.51 [0.02 to 0.83]

| 0.81 [0.24 to 1.56]

| 1.15 [0.45 to 1.84]

|-
| colspan="12"| '''East Asian Monsoon (June–July–August, JJA)'''

|-
| Precipitation

| 5.59 [4.47 to 6.86]

| 0.37 [–0.09 to +0.93]

| 0.37 [–0.09 to +0.87]

| 0.34 [0.05 to 0.76]

| 0.22 [–0.16 to +0.88]

| 0.43 [0.03 to 1.1]

| 0.43 [0.07 to 1.02]

| 0.44 [–0.0 to +1.08]

| 0.51 [0.11 to 1.09]

| 0.59 [0.02 to 1.31]

| 0.84 [0.24 to 1.74]

|-
| Runoff

| 2.24 [1.28 to 3.41]

| 0.11 [–0.16 to +0.4]

| 0.13 [–0.19 to +0.42]

| 0.13 [–0.15 to +0.4]

| 0.15 [–0.29 to +0.76]

| 0.2 [–0.11 to +0.72]

| 0.16 [–0.08 to +0.49]

| 0.16 [–0.13 to +0.58]

| 0.22 [–0.13 to +0.64]

| 0.36 [–0.05 to +0.87]

| 0.51 [0.06 to 1.24]

|-
| P–E

| 2.41 [1.51 to 3.31]

| 0.1 [–0.31 to +0.51]

| 0.13 [–0.2 to +0.48]

| 0.17 [–0.04 to +0.53]

| 0.17 [–0.2 to +0.75]

| 0.23 [–0.09 to +0.86]

| 0.16 [–0.07 to +0.57]

| 0.18 [–0.18 to +0.65]

| 0.24 [–0.1 to +0.76]

| 0.4 [–0.08 to +0.93]

| 0.5 [–0.13 to +1.34]

|-
| colspan="12"| '''North American Monsoon (July–August–September, JAS)'''

|-
| Precipitation

| 3.05 [2.24 to 3.96]

| 0.13 [–0.08 to +0.43]

| 0.07 [–0.27 to +0.32]

| 0.02 [–0.32 to +0.41]

| –0.03 [–0.37 to +0.38]

| –0.03 [–0.43 to +0.52]

| 0.18 [–0.05 to +0.44]

| 0.04 [–0.35 to +0.39]

| –0.1 [–0.51 to +0.37]

| –0.19 [–0.76 to +0.44]

| –0.15 [–0.96 to +0.57]

|-
| Runoff

| 0.46 [0.09 to 0.87]

| 0.03 [–0.04 to +0.12]

| 0.03 [–0.07 to +0.16]

| 0.02 [–0.1 to +0.14]

| –0.0 [–0.1 to +0.14]

| –0.0 [–0.11 to +0.14]

| 0.04 [–0.03 to +0.15]

| –0.0 [–0.19 to +0.15]

| –0.03 [–0.22 to +0.14]

| –0.05 [–0.23 to +0.19]

| –0.06 [–0.29 to +0.23]

|-
| P–E

| 0.78 [–0.1 to +1.45]

| 0.06 [–0.1 to +0.2]

| 0.02 [–0.18 to +0.24]

| 0.0 [–0.22 to +0.23]

| –0.03 [–0.24 to +0.2]

| –0.04 [–0.31 to +0.27]

| 0.09 [–0.06 to +0.31]

| 0.01 [–0.22 to +0.25]

| –0.08 [–0.28 to +0.25]

| –0.17 [–0.68 to +0.25]

| –0.18 [–0.72 to +0.38]

|-
| colspan="12"| '''South American Monsoon (December–January–February, DJF)'''

|-
| Precipitation

| 8.44 [5.98 to 10.22]

| 0.09 [–0.2 to +0.3]

| 0.12 [–0.29 to +0.62]

| 0.09 [–0.47 to +0.62]

| 0.07 [–0.55 to +0.62]

| 0.07 [–0.5 to +0.71]

| 0.02 [–0.32 to +0.36]

| 0.09 [–0.33 to +0.58]

| 0.07 [–0.63 to +0.81]

| 0.05 [–1.17 to +0.82]

| –0.0 [–1.22 to +1.19]

|-
| Runoff

| 2.49 [1.11 to 4.38]

| –0.02 [–0.23 to +0.26]

| –0.01 [–0.43 to +0.53]

| 0.01 [–0.45 to +0.46]

| –0.03 [–0.49 to +0.36]

| –0.03 [–0.56 to +0.53]

| –0.04 [–0.27 to +0.28]

| –0.01 [–0.41 to +0.39]

| –0.01 [–0.58 to +0.55]

| –0.06 [–0.81 to +0.24]

| –0.04 [–0.85 to +0.93]

|-
| P–E

| 4.5 [2.83 to 6.01]

| 0.04 [–0.23 to +0.25]

| 0.08 [–0.26 to +0.47]

| 0.04 [–0.43 to +0.53]

| 0.04 [–0.5 to +0.61]

| 0.02 [–0.45 to +0.58]

| –0.01 [–0.32 to +0.29]

| 0.03 [–0.34 to +0.43]

| –0.02 [–0.63 to +0.62]

| –0.02 [–1.03 to +0.72]

| –0.09 [–1.11 to +0.98]

|-
| colspan="12"| '''Australian and Maritime Continent Monsoon (December–January–February, DJF)'''

|-
| Precipitation

| 8.63 [6.79 to 10.7]

| 0.26 [0.04 to 0.49]

| 0.22 [–0.23 to +0.53]

| 0.28 [–0.2 to +0.79]

| 0.25 [–0.14 to +0.73]

| 0.38 [0.0 to 0.84]

| 0.15 [–0.09 to +0.34]

| 0.24 [–0.36 to +0.74]

| 0.5 [–0.1 to +1.07]

| 0.65 [–0.08 to +1.33]

| 0.9 [0.09 to 1.76]

|-
| Runoff

| 3.82 [1.78 to 7.25]

| 0.2 [–0.01 to +0.48]

| 0.23 [–0.11 to +0.48]

| 0.29 [–0.11 to +0.7]

| 0.24 [–0.13 to +0.56]

| 0.35 [–0.03 to +0.87]

| 0.12 [–0.06 to +0.39]

| 0.29 [–0.08 to +0.88]

| 0.49 [0.09 to 1.25]

| 0.61 [–0.09 to +1.05]

| 0.92 [0.14 to 1.83]

|-
| P–E

| 4.8 [3.19 to 6.63]

| 0.22 [0.03 to 0.47]

| 0.13 [–0.23 to +0.42]

| 0.2 [–0.16 to +0.7]

| 0.2 [–0.14 to +0.62]

| 0.27 [–0.09 to +0.61]

| 0.12 [–0.1 to +0.31]

| 0.16 [–0.31 to +0.54]

| 0.38 [–0.05 to +0.75]

| 0.54 [–0.08 to +1.13]

| 0.69 [0.09 to 1.27]

|-
| colspan="12"| '''West African Monsoon (June–July–August–September, JJAS)'''

|-
| Precipitation

| 5.14 [3.62 to 7.18]

| 0.16 [–0.19 to +0.4]

| 0.14 [–0.22 to +0.56]

| 0.24 [–0.14 to +0.72]

| 0.3 [–0.1 to +0.85]

| 0.38 [–0.12 to +1.24]

| 0.06 [–0.25 to +0.52]

| 0.1 [–0.25 to +0.57]

| 0.25 [–0.32 to +0.91]

| 0.38 [–0.49 to +1.14]

| 0.49 [–0.55 to +1.56]

|-
| Runoff

| 1.43 [0.34 to 2.57]

| 0.06 [–0.07 to +0.22]

| 0.05 [–0.18 to +0.27]

| 0.14 [–0.13 to +0.54]

| 0.2 [–0.05 to +0.7]

| 0.24 [–0.1 to +0.8]

| –0.01 [–0.2 to +0.21]

| 0.03 [–0.25 to +0.35]

| 0.1 [–0.25 to +0.51]

| 0.25 [–0.28 to +0.85]

| 0.3 [–0.33 to +0.93]

|-
| P–E

| 2.41 [1.05 to 4.07]

| 0.08 [–0.2 to +0.35]

| 0.1 [–0.2 to +0.4]

| 0.2 [–0.11 to +0.63]

| 0.23 [–0.11 to +0.74]

| 0.36 [–0.06 to +1.11]

| –0.01 [–0.27 to +0.35]

| 0.07 [–0.2 to +0.44]

| 0.18 [–0.21 to +0.6]

| 0.28 [–0.38 to +0.95]

| 0.46 [–0.44 to +1.4]

|}

<div id="_idContainer063" class="Basic-Text-Frame"></div>

[[File:3be7e22b5339e28a893382bf3347666c IPCC_AR6_WGI_Figure_8_22.png]]

'''Figure 8.22 |''' '''Projected regional monsoons precipitation changes.''' Percentage change in projected seasonal mean precipitation over regional monsoon domains (as defined in Figure 8.11, [[#8.3.2.4|Section 8.3.2.4]] and Annex V) for near term (2021–2040), mid-term (2041–2060), and long term (2081–2100) periods based on 24 CMIP6 models and three SSP scenarios (SSP1-2.6, SSP2-4.5 and SSP5-8.5). Further details on data sources and processing are available in the chapter data table (Table 8.SM.1).

<div id="8.4.2.4.1" class="h4-container"></div>

<span id="south-and-south-east-asian-monsoon-1"></span>
===== 8.4.2.4.1 South and South East Asian Monsoon =====

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In AR5, South and South East Asian monsoon (SAsiaM) precipitation was projected to increase by the end of the 21st century but with a weakening of the circulation, with ''high agreement'' across the CMIP5 models (Kitoh et al. , 2013; Menon et al. , 2013; Sharmila et al. , 2015; Sooraj et al. , 2015; [[#Kitoh--2017|Kitoh, 2017]] ; Kulkarni et al. , 2020) . Since AR5, most studies have confirmed projected increases in South Asian monsoon precipitation ( ''high confidence'' ), while one high-resolution model (35 km in latitude/longitude) projects monsoon precipitation decreases during the 21st century following the RCP4.5 scenario ( [[#Krishnan--2016|Krishnan et al., 2016]] ).

Over South Asia, the moisture-bearing monsoon low-level jet is projected to shift northward in CMIP3 and CMIP5 models ( [[#Sandeep--2015|Sandeep and Ajayamohan, 2015]] ). Greater warming over the Asian land region compared to the ocean contributes to intensification of the monsoon low-level south-westerly winds and precipitation ( [[#Endo--2018|Endo et al., 2018]] ), even though the combined effect of upper and lower tropospheric warming makes the Asian monsoon circulation response rather complicated. A high resolution model projection, based on the RCP8.5 scenario, indicates that a northward shift of the low-level jet and associated weakening of the large-scale monsoon circulation can induce a large reduction in the genesis of monsoon low pressure systems by the late 21st century ( [[#Sandeep--2018|Sandeep et al., 2018]] ). Experiments with constant forcing indicate that at 1.5°C and 2°C global warming levels, mean precipitation and monsoon extremes are projected to intensify in summer over India and South Asia ( [[#Chevuturi--2018|Chevuturi et al., 2018]] ; D. [[#Lee--2018|]] [[#Lee--2018|Lee et al., 2018]] ) and that a 0.5°C difference would imply a 3% increase of precipitation ( [[#Chevuturi--2018|Chevuturi et al., 2018]] ). CMIP5 models project an increase in short intense active days and decrease in long active days, with no significant change in the number of break spells for India ( [[#Sudeepkumar--2018|Sudeepkumar et al., 2018]] ).

Future monsoon projections from CMIP6 models show an increase of SAsiaM precipitation across all the scenarios and across all the time frames (Figure 8.22) with the maximum increase at the end of the 21st century in SSP5-8.5 (Almazroui et al. , 2020c; Z. Chen et al. , 2020b; Ha et al. , 2020; Wang et al. , 2021) . Table 8.2 confirms that changes in runoff and P–E over SAsiaM region are positive and largest in the higher emissions scenarios considered, as in precipitation. On the other hand, changes in the ensemble mean for all the variables considered in the SSP1-1.9 scenario are negative for both mid and long-term periods (Table 8.2). This is also consistently reflected in the spatial map of future precipitation changes (Figure 8.15). Different near-term projections of the SAsiaM may result given the diversity in the future aerosol emissions pathways and policies for regulating air pollution ( [[#Wilcox--2020|Wilcox et al., 2020]] ). Additionally, near-term projections of SAsiaM precipitation are expected to be constrained by internal variability associated with the PDV (X. [[#Huang--2020|Huang et al., 2020]] a). CMIP6 models also indicate a lengthening of the summer monsoon over India by the end of the 21st century, at least in SSP2-4.5, with considerable inter-model spread in the projected late retreat ( [[#Ha--2020|Ha et al., 2020]] ).

In summary, consistent with AR5, there is ''high confidence'' that SAsiaM precipitation is projected to increase during the 21st century in response to continued global warming across the CMIP6 higher emissions scenarios, mostly in the mid- and long terms.

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===== 8.4.2.4.2 East Asian Monsoon =====

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In AR5, the East Asian monsoon (EAsiaM) was projected to intensify in terms of precipitation, with an earlier onset and longer duration of the summer season. Since AR5, there has been improved understanding of future projected changes in the EAsiaM.

CMIP5 projections indicated a possible intensification of the EAsiaM circulation during the 21st century, in addition to precipitation increase, although there is a lack of consensus on changes in the western North Pacific subtropical high, this is an important feature of the EAsiaM circulation ( [[#Kitoh--2017|Kitoh, 2017]] ). Furthermore, the EAsiaM precipitation enhancements in the CMIP5 projections are prominent over the southern part of the Baiu rainband by the late 21st century, with no significant changes in the Meiyu precipitation over central-eastern China ( [[#Horinouchi--2019|Horinouchi et al., 2019]] ). It was also shown that the Baiu precipitation response in CMIP5 projections is accompanied by a southward retreat of the western North Pacific subtropical high and a southward shift of the East Asian subtropical jet ( [[#Horinouchi--2019|Horinouchi et al., 2019]] ). According to the high-resolution MRI-AGCM global warming experiments, future summer precipitation could potentially increase on the southern side and decrease on the northern side of the present-day Baiu location in response to downward-motion tendencies which can offset the ‘wet-gets-wetter’ effect, but is subject to large model uncertainties ( [[#Ose--2019|Ose, 2019]] ). Future projections of land warming over the Eurasian continent ( [[#Endo--2018|Endo et al., 2018]] ) and intensified land – sea thermal contrast (Z. [[#Wang--2016|]] [[#Wang--2016|]] [[#Wang--2016|Wang et al., 2016]] ; [[#Tian--2019|Tian et al., 2019]] ) can potentially intensify the EAsiaM circulation during the 21st century. However, there are large uncertainties in projected water cycle changes over the region ( [[#Endo--2018|Endo et al., 2018]] ), mostly in the near-term because of uncertainties in future aerosol emissions scenarios ( [[#Wilcox--2020|Wilcox et al., 2020]] ), as well as due to the interplay between internal variability and anthropogenic external forcing ( [[#Wang--2021|Wang et al., 2021]] ).

Inter-hemispheric mass exchange can act as a bridge connecting SH circulation with EAsiaM rainfall, however this inter-hemispheric link is projected to weaken in a future warmer climate as seen from a CCSM4 projection using the RCP8.5 scenario ( [[#Yu--2018|Yu et al., 2018]] ). A comparison of 1.5°C and 2°C global warming levels reveals how a 0.5°C difference could result in precipitation enhancement over large areas of East Asia ( D. Lee et al. , 2018; J. Liu et al. , 2018; Chen et al. , 2019 ), with substantial increases in the frequency and intensity of extremes ( [[#Chevuturi--2018|Chevuturi et al., 2018]] ; D. [[#Li--2019|]] [[#Li--2019|Li et al., 2019]] ). Future monsoon projections from the CMIP6 models show increase of EAsiaM precipitation across all the scenarios (Z. [[#Chen--2020|]] [[#Chen--2020|Chen et al., 2020]] b), though with a large model spread mostly on the long-term and in the higher emissions scenarios (Figure 8.22). Considering all the five scenarios used across the report, changes in precipitation, runoff and P–E over the EAsiaM are positive and become larger for highest emissions scenarios and for the long-term mean, except for the mid-term SSP1-1.9 scenario where the changes are close to zero or even negative (Table 8.2). Additionally, CMIP6 models confirm a projected increased length of the EAsiaM season due to early onset and late retreat ( [[#Ha--2020|Ha et al., 2020]] ).

In summary, despite the uncertainties in the monsoon circulation response in CMIP5 and CMIP6 models, there is ''high confidence'' that summer monsoon precipitation over East Asia will increase in the 21st century and ''medium confidence'' that the monsoon season will be longer.

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===== 8.4.2.4.3 West African Monsoon =====

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The AR5 concluded that projections of West African monsoon (WAfriM) rainfall are highly uncertain in CMIP3 and CMIP5 models, but still suggest a small delay and intensification in late wet season rains. Studies published since AR5 are broadly consistent with this assessment. CMIP6 models agree on statistically significant projected increases in rainfall in eastern-central Sahel and a decrease in the west for the end of the 21st century ( [[#Roehrig--2013|Roehrig et al., 2013]] ; [[#Biasutti--2019|Biasutti, 2019]] ; [[#Monerie--2020|Monerie et al., 2020]] ). However, the magnitude of WAfriM projected precipitation depends on the convective parametrization used ( [[#Hill--2017|Hill et al., 2017]] ), and large uncertainties remain in WAfriM projections because of large inter-model spread, particularly over the western Sahel ( [[#Roehrig--2013|Roehrig et al., 2013]] ; [[#Biasutti--2019|Biasutti, 2019]] ; [[#Monerie--2020|Monerie et al., 2020]] ). CMIP6 models show a general increase of WAfriM precipitation across all future scenarios but with a substantial model spread for the SSP5-8.5 scenario (Figure 8.22). This sensitivity arises from the combined and contrasting influences of anthropogenic greenhouse gas and aerosol forcing that affect WAfriM precipitation (particularly over the Sahel) directly and also indirectly through subtropical North Atlantic SST changes ( [[#Giannini--2019|Giannini and Kaplan, 2019]] ) . The large model spread and associated uncertainties in projected precipitation changes is reflected also in runoff and P–E changes (Table 8.2). Regional climate models (RCMs) ensembles (e.g., [[#Klutse--2018|Klutse et al., 2018]] ) agree with CMIP5 projected rainfall trends but some individual models show rainfall declines (e.g., [[#Sylla--2015|Sylla et al., 2015]] ; [[#Akinsanola--2018|Akinsanola et al., 2018]] ), highlighting the existing large uncertainties in RCMs WAfriM rainfall projections.

Changes in seasonality (Box 8.2) are projected with a later monsoon onset ( ''high confidence'' ) over the Sahel and a late cessation ( ''medium confidence'' ), suggesting a delayed wet season as a regional response to global GHG forcing ( [[#Biasutti--2013|Biasutti, 2013]] ; [[#Dunning--2018|Dunning et al., 2018]] ; [[#Akinsanola--2019|Akinsanola and Zhou, 2019]] ). Rainfall distribution is projected to be highly variable with a decrease in the number of rainy days in the western Sahel, consistent with an increase in consecutive dry days and a reduction in the number of growing season days ( [[#Cook--2012|Cook and Vizy, 2012]] ; [[#Diallo--2016|Diallo et al., 2016]] ). A decrease in the frequency but an increase in the intensity of very wet events is projected to be more pronounced over the Sahel than over Guinean coast, and also under higher emissions scenarios (i.e., RCP8.5; [[#Sylla--2015|Sylla et al., 2015]] ; [[#Akinsanola--2018|Akinsanola et al., 2018]] ).

In summary, post-AR5 studies and newly available CMIP6 results indicate projected rainfall increases in the eastern-central WAfriM region but decreases in the west ( ''high confidence'' ), with a delayed wet season ( ''medium confidence'' ). Overall, WAfriM summer precipitation is projected to increase during the 21st century but with larger uncertainty noted under high-emissions scenarios ( ''medium co'' ''nfidence'' ).

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===== 8.4.2.4.4 North American Monsoon =====

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The AR5 concluded that the North American monsoon (NAmerM) will ''likely'' intensify in the future, even though there is ''low agreement'' among models. The AR5 reported ''medium confidence'' that precipitation associated with the NAmerM will arrive later in the annual cycle and persist longer.

Since AR5, analyses of CMIP5 projections suggest little change in the overall amount of NAmerM precipitation in response to rising global surface temperature. However, significant declines are projected in the early monsoon season and increases in the late monsoon season, suggesting a shift in seasonality toward a delayed monsoon onset and demise ( [[#Cook--2013|Cook et al., 2013]] ). It is recognized that CMIP5 models are generally too coarsely-resolved to simulate the Gulf of California and the moisture surges associated with the NAmerM ( [[#Pascale--2017|Pascale et al., 2017]] ). Under different RCPs, CMIP5 models tend to project a reduction in NAmerM precipitation but an increase in extreme precipitation events (Torres-Alavez et al. , 2014; Bukovsky et al. , 2015; Pascale et al. , 2019) . The almost unchanged or slight decrease in NAmerM total precipitation amount under global warming projections is at odds with paleoclimate records that suggest increased monsoon precipitation under past warm conditions ( [[#D’Agostino--2019|D’Agostino et al., 2019]] ; [[#Seth--2019|Seth et al., 2019]] ). However, there is ''low agreement'' on how those changes and the mechanisms that drive them are affected under different RCPs since most simulations are model-dependant ( [[#Cook--2013|Cook and Seager, 2013]] ; [[#Geil--2013|Geil et al., 2013]] ; [[#Pascale--2019|Pascale et al., 2019]] ). Projections from six CMIP6 models show a shortening of the NAmerM under the SSP5-8.5 scenario due to earlier demises ( [[#Moon--2020|Moon and Ha, 2020]] ). In addition, CMIP6 projections show a decrease in NAmerM precipitation under SSP2-4.5 and SSP5-8.5 scenarios by the end of the 21st century with large inter-model spread (Figure 8.22). This result is also supported by the analysis of 31 CMIP6 models under the SSTP5-8.5 scenario for the 2080 – 2099 period ( [[#Almazroui--2021|Almazroui et al., 2021]] ). Non-linearities and uncertainties in the NAmerM projected changes are valid for many water cycle variables, like precipitation, runoff and P–E (Table 8.2).

In summary, there is ''low agreement'' on a projected decrease of NAmerM precipitation, however there is ''high confidence'' in delayed onsets and demises of the summer monsoon.

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===== 8.4.2.4.5 South American Monsoon =====

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The AR5 reported ''medium confidence'' that the South American monsoon (SAmerM) overall precipitation will remain unchanged, and ''medium confidence'' in projections of extreme precipitation. The AR5 also stated ''high confidence'' in the spatial expansion of the SAmerM, resulting from increased temperature and humidity.

Since AR5, some studies indicate that the SAmerM would experience changes in its seasonal cycle, with delayed monsoon onsets under increasing GHG emissions associated to different RCPs (Fu et al. , 2013; Reboita et al. , 2014; Boisier et al. , 2015; Pascale et al. , 2016; Seth et al. , 2019; [[#Sena--2020|Sena and Magnusdottir, 2020]] ) . In contrast, other studies indicate projected earlier onsets and delayed retreats of the SAmerM under the RCP8.5 scenario based on six CMIP5 models ( [[#Jones--2013|Jones and Carvalho, 2013]] ). These differences have been linked to the methodology used to determine monsoon timing, and sensitivity to the monsoon domain considered ( [[#8.3.2.4.5|Section 8.3.2.4.5]] ; [[#Correa--2021|Correa et al., 2021]] ). Recent studies provide further evidence for the projection of delayed SAmerM onsets by the late 21st century ( [[#Sena--2020|Sena and Magnusdottir, 2020]] ). An analysis of six CMIP6 models under the SSP5-8.5 scenario confirm the projections of delayed SAmerM onsets by the end of the 21st century ( [[#Moon--2020|Moon and Ha, 2020]] ). In addition, projected changes in the intensity and length of the SAmerM season have been found to be model-dependent ( [[#Pascale--2019|Pascale et al., 2019]] ). The analysis of CMIP5 projections of total monsoon rainfall indicate mixed signals in the Amazon and SAmerM regions ( [[#Jones--2013|Jones and Carvalho, 2013]] ; [[#Marengo--2014|Marengo et al., 2014]] ), with some studies suggesting increased summer precipitation in the core SAmerM region ( [[#Kitoh--2013|Kitoh et al., 2013]] ; [[#Seth--2013|Seth et al., 2013]] ). Dynamical downscaling of CMIP5 projections under the RCP4.5 and RCP8.5 scenarios with the Eta RCM suggests reductions of austral summer precipitation over the SAmerM region throughout the 21st century ( [[#Chou--2014|Chou et al., 2014]] ). Further analysis using 15 different CMIP6 models for the SSP2-4.5 scenario suggest reductions in total SAmerM rainfall ( [[#Wang--2020|]] [[#Wang--2020|]] [[#Wang--2020|]] [[#Wang--2020|B. Wang et al., 2020]] ). However, other analyses of CMIP6 projections under different SSP scenarios do not report clear changes in the SAmerM precipitation throughout the 21st century (Figure 8.22; Z. [[#Chen--2020|]] [[#Chen--2020|Chen et al., 2020]] b; [[#Jin--2020|Jin et al., 2020]] ). Similar uncertainties for all the SSP scenarios used across the report are found for other water cycle variables, including runoff and P–E (Table 8.2). Furthermore, there is disagreement in projected extreme precipitation in the region, with some CMIP5-based studies suggest reductions ( [[#Marengo--2014|Marengo et al., 2014]] ), while others indicate increases based on CMIP5 and CMIP6 models ( [[#Kitoh--2013|Kitoh et al., 2013]] ; [[#Sena--2020|Sena and Magnusdottir, 2020]] ).

In summary, there is ''high confidence'' that the SAmerM will experience delayed onsets in association with increases in GHG. However, there is ''low agreement'' on the projected changes in terms of total precipitation of the South American summer monsoon season.

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===== 8.4.2.4.6 Australian and Maritime Continent Monsoon =====

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The AR5 concluded that projected changes in Australian and Maritime Continent monsoon (AusMCM) rainfall and seasonality are uncertain in the CMIP5 models, with some projecting increases and others projecting decreases for the range of emissions scenarios. Models that perform better at simulating present day regional climate project little change or an increase in Australian monsoon rainfall ( [[#Jourdain--2013|Jourdain et al., 2013]] ; CSIRO and BoM, 2015; [[#Brown--2016b|Brown et al., 2016b]] ). CMIP6 models project increased AusMCM precipitation in the 21st century but with a more robust signal in SSP2-4.5 and SSP5-8.5 rather than in lower emissions scenarios (Figure 8.22). A reduced range of CMIP6 rainfall projections but continued disagreement on the sign of change is reported over Australia ( [[#Narsey--2020|Narsey et al., 2020]] ).

The northern and eastern parts of the Maritime Continent have projected increases in rainfall in CMIP5 models ( [[#Siew--2014|Siew et al., 2014]] ), while there are projected decreases over Java, Sulawesi and southern parts of Borneo and Sumatra. Rainfall changes are correlated with the extent of warming in the western tropical Pacific in CMIP5 models ( [[#Brown--2016b|Brown et al., 2016b]] ) but inter-model differences are also related to modelled large-scale zonal mean precipitation response in both CMIP5 and CMIP6 model ensembles ( [[#Narsey--2020|Narsey et al., 2020]] ). Decomposition of projected rainfall changes indicates that the largest source of model uncertainty is associated with shifts in the spatial pattern of convection ( [[#Chadwick--2013|Chadwick et al., 2013]] ; [[#Brown--2016b|Brown et al., 2016b]] ). Uncertainties in capturing the spatial and temporal features of the Maritime Continent monsoon depend also on the horizontal resolution of coupled climate models (e.g., [[#Jourdain--2013|Jourdain et al., 2013]] ).

The role of anthropogenic aerosol forcing in future projections of the Australian monsoon has been investigated for CMIP5 models ( [[#Dey--2019a|Dey et al., 2019a]] ); decreases in anthropogenic aerosol concentrations over the 21st century are expected to produce relatively greater warming in the NH than SH, favouring a northward shift of the tropical rain belt (e.g., [[#Rotstayn--2015|Rotstayn et al., 2015]] ).

There are some clear projected changes in the rainfall variability and extremes of the Australian monsoon. Rainfall variability in the Australian monsoon domain increases on time scales from daily to decadal in CMIP5 models ( [[#Brown--2017|Brown et al., 2017]] ), indicating either more intense wet days or more dry days or both. There is also a projected increase in the intensity of extreme rainfall but a reduction in the frequency of heavy rainfall days for the Australian monsoon (Dey et al. , 2019a) . This is consistent with [[#Moise--2020|Moise et al. (2020)]] , who found an increase in Australian monsoon active phase or ‘burst’ rainfall intensity but a reduction in the number of burst days and events.

H. [[#Zhang--2013|]] [[#Zhang--2013|Zhang et al. (2013)]] examined changes in Australian monsoon onset and duration in CMIP3 models and found model agreement on a delay in onset and shortened duration to the north of Australia, but less agreement over the interior of the continent. An updated study of CMIP5 models found similar mean changes with delayed onset and shortened duration, but substantial model disagreement (H. [[#Zhang--2016|]] [[#Zhang--2016|]] [[#Zhang--2016|]] [[#Zhang--2016|Zhang et al., 2016]] ).

In summary, CMIP6 projections show an increase of AusMCM precipitation across all emissions scenarios. There is strong model agreement on an increase in monsoon precipitation over the Maritime Continent while there is ''low agreement'' on the direction of change over northern Australia. There is a projected increase in rainfall variability over northern Australia, with increased intensity of rainfall during the active or ‘burst’ phase ( ''medium con'' ''fidence'' ).

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<span id="tropical-cyclones-1"></span>
==== 8.4.2.5 Tropical Cyclones ====

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Tropical cyclones (TCs) projections are primarily assessed in [[IPCC:Wg1:Chapter:Chapter-11#11.7.1.5|Section 11.7.1.5]] . Here, we extend this analysis by assessing the implications of projected changes in tropical cyclones on the water cycle.

The AR5 concluded that TC rainfall rate was ''likely'' to increase through the 21st century. [[IPCC:Wg1:Chapter:Chapter-11#11.7.1.5|Section 11.7.1.5]] assesses that the average tropical cyclone rain-rate is projected to increase with warming ( ''high confidence'' ), and peak rain rates are projected to increase at greater than the Clausius–Clapeyron scaling rate of 7% °C <sup>–1</sup> warming in some regions due to increased low-level moisture convergence ( ''medium confidence'' ). The increase in TC rainfall rate is explained by increased TC intensity resulting from increasing SSTs, and increased environmental water vapour ( [[#Chauvin--2017|Chauvin et al., 2017]] ; M. [[#Liu--2019|]] [[#Liu--2019|Liu et al., 2019]] ).

Consistent with the observed poleward migration of tropical cyclone activity ( [[#Kossin--2014|Kossin et al., 2014]] ), in the SH a larger proportion of storms are projected to decay south of 25°S at the end of the 21st century but with negligible changes in genesis latitude and storm duration for the Australian region (CSIRO and BoM, 2015; [[#Sharmila--2018|Sharmila and Walsh, 2018]] ) . An analysis of projections for North Pacific islands indicate that the maximum intensity of storms will increase but the number of tropical cyclones will decrease in some places, such as Guam and Kwajalein Atoll in the tropical north-western Pacific, or remain the same in other regions like near Okinawa (Japan) or Oahu (Hawaii) ( [[#Widlansky--2019|Widlansky et al., 2019]] ). TC-induced storm tides affecting landfall in the Pearl River delta over South China are projected to increase by the end of the 21st century (J. [[#Chen--2020|]] [[#Chen--2020|Chen et al., 2020]] b)

In summary, there is ''high confidence'' that heavy precipitation associated with tropical cyclones is projected to increase, in response to well-understood processes related to increased low-level moisture convergence and environmental water vapour.

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==== 8.4.2.6 Stationary Waves ====

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The AR5 did not provide an assessment of stationary wave projections as distinct from other related aspects of circulation, such as blocking, modes of variability, and storm tracks. Here we provide a brief assessment of stationary wave projections from the water cycle perspective, with the related circulation aspects considered separately in the following sections.

Several studies based on CMIP5 projections show changes in NH winter stationary waves that increase precipitation over the west coast of North America and decrease it over the eastern Mediterranean and parts of south-western North America ( Neelin et al. , 2013; Seager et al. , 2014a, b, 2019b; Simpson et al. , 2016; Wills et al. , 2019 ), although the underlying dynamics are not yet fully understood ( [[#Seager--2019b|Seager et al., 2019b]] ; [[#Wills--2019|Wills et al., 2019]] ). For the NH winter global teleconnection pattern, the majority of the models analyzed in ( [[#Sandler--2020|Sandler and Harnik, 2020]] ) project the development of a preferred longitudinal phasing for the pattern, but with strong disagreement among models over the details of the phasing and therefore the associated regional hydrologic impacts.

While the potential role of increasing hydrologic extremes with quasi-resonant stationary waves during NH summer has received considerable attention (see [[#8.3.2.6|Section 8.3.2.6]] ), as yet there is no clear evidence in model projections that this variability will increase ( [[#Teng--2019|Teng and Branstator, 2019]] ). The influence of the Arctic on mid-latitude circulation is assessed in Cross-Chapter Box 10.1, which reports that there is ''low confidence'' in the dominant contribution of Arctic warming compared to other drivers in future projections. Potential changes to the stratospheric polar vortex in CMIP5 models have a substantial influence on tropospheric stationary waves and associated hydrologic impacts in both the NH ( [[#Zappa--2017|Zappa and Shepherd, 2017]] ) and SH ( [[#Mindlin--2020|Mindlin et al., 2020]] ). CMIP5 models have some important limitations in their representation of stationary waves ( [[#Lee--2013|Lee and Black, 2013]] ; [[#Simpson--2016|Simpson et al., 2016]] ; [[#Garfinkel--2020|Garfinkel et al., 2020]] ) and this aspect of CMIP6 models has not yet been comprehensively evaluated.

In summary, future changes in stationary waves may have an important influence on both the mean state and variability of the water cycle. Limitations in model representation, dynamical understanding, and the number of targeted studies on the topic currently constrain the assessment of future changes in stationary waves. Based on current knowledge, there is ''low confidence'' that projected changes in stationary wave activity will contribute to decreases of cold season precipitation over the eastern Mediterranean and increases over the west coast of North America.

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==== 8.4.2.7 Atmospheric Blocking ====

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In AR5, the increased ability of models to simulate blocking and higher agreement on projections led to an assessment with ''medium confidence'' that the frequency of NH and SH blocking will not increase, but future changes in blocking intensity and persistence were deemed uncertain (AR5 Chapter 14, ES and Box 14.2). Blocking influences precipitation (e.g., [[#Trigo--2004|Trigo et al., 2004]] ), flooding (e.g., Yamada et al. , 2016) , drought (e.g., Dong et al. , 2018b) , snow (e.g., [[#García-Herrera--2006|García-Herrera and Barriopedro, 2006]] ) , and glacier melt (e.g., [[#Hanna--2013|Hanna et al., 2013]] ), and so is of broad importance to the water cycle in areas of blocking activity.

Blocking projections are assessed in this Report in [[IPCC:Wg1:Chapter:Chapter-4|Chapter 4]] ( [[IPCC:Wg1:Chapter:Chapter-4#4.5.1.6|Section 4.5.1.6]] ), and model performance in simulating blocking is also discussed in [[IPCC:Wg1:Chapter:Chapter-3|Chapter 3]] ( [[IPCC:Wg1:Chapter:Chapter-3#3.3.3.3|Section 3.3.3.3]] ). CMIP5 projections suggest a complex response in blocking frequencies with an eastward shift in NH winter blocking, mid-latitude decreases in boreal summer except in eastern Europe – western Russia, and SH decreases in the Pacific sector during austral spring and summer. CMIP6 projections (Figure. 4.28) show a notable decrease in blocking activity over Greenland and the North Pacific for the SSP3-7.0 and SSP5-8.5 scenarios. However, the continued large differences among current models as well as the sensitivity to blocking detection methods limits confidence in projected regional changes in blocking (see also [[IPCC:Wg1:Chapter:Chapter-10#10.3.3.3.1|Section 10.3.3.3.1]] ). The influence of blocking on multiple elements of the water cycle means that the uncertainty in blocking projections adds a corresponding layer of uncertainty to water cycle projections.

In summary, and despite recent improvements in the simulation of blocking, there is ''limited evidence'' in model projections of future changes, except for boreal winter over Greenland and the North Pacific where there is high confidence that blocking events are not expected to increase in the SSP3-7.0 and SSP5-8.5 scenarios. As with stationary waves, this adds uncertainty to mid-latitude water cycle projections at the regional scale.

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==== 8.4.2.8 Extratropical Cyclones, Storm Tracks and Atmospheric Rivers ====

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<span id="extratropical-cyclones-and-storm-tracks-1"></span>
===== 8.4.2.8.1 Extratropical cyclones and storm tracks =====

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The AR5 found that extratropical storms were expected to decrease in the Northern Hemisphere (NH), but only by a few percent. Meanwhile, precipitation associated with extratropical storms was projected to increase due to thermodynamic increases in moisture but potentially also due to intensification from increased latent heat release. Latent heating is a strong influence on extratropical storms, so it is plausible that changes in precipitation and associated latent heating could affect extratropical storm intensity and thus precipitation (Z. Zhang et al., 2019) .

There is increased evidence that precipitation associated with individual extratropical storms is projected to increase, following thermodynamic drivers with negligible dynamic change ( [[#Yettella--2017|Yettella and Kay, 2017]] ). Comparisons with reanalyses also support the projected increase in thermodynamic precipitation with little dynamic response for precipitation associated with extratropical storms ( [[#Li--2014|Li et al., 2014]] ). There is ''high confidence'' that projected increases inprecipitation associated with extratropical storms in the NH (Marciano et al. , 2015; Pepler et al. , 2016; Michaelis et al. , 2017; [[#Yettella--2017|Yettella and Kay, 2017]] ; [[#Zhang--2017|Zhang and Colle, 2017]] ; Hawcroft et al. , 2018; Kodama et al. , 2019) . A projected decrease in the number of extratropical cyclones over the NH during the boreal summer in CMIP5 models was reported by [[#Chang--2016|Chang et al. (2016)]] who related this decrease with a decrease in cloudiness and thus accentuating increased maximum temperatures. However, model spread was quite large, especially over North America, thus there is only ''low confidence'' in this seasonal signal.

In AR5, the Southern Hemisphere (SH) storm track was deemed ''likely'' to shift poleward, the North Pacific storm track ''more likely than not'' to shift poleward, while the North Atlantic storm track was ''unlikely'' to display any discernible changes. There was ''low confidence'' in regional storm track changes and the associated surface climate impacts, although a weakening of the Mediterranean storm track was a robust response of the models. Since AR5, the SH mid-latitude storm track is projected to shift poleward and the westerlies are projected to strengthen over Australia (CSIRO and BoM, 2015). Although thermodynamic effects were considered to be the most important factor in overall projections of increased mid-latitude precipitation, the general poleward shift in cyclogenesis and an enhanced latitudinal displacement of individual cyclones may play a role ( [[#Tamarin-Brodsky--2017|Tamarin-Brodsky and Kaspi, 2017]] ).

In AR5, several factors were identified as relevant to the uncertainties in projections of cyclone intensity, frequency, location of storm tracks and precipitation associated with ETCs. These include horizontal resolution, resolution of the stratosphere, and how changes in the Atlantic meridional overturning circulation (AMOC) were simulated. Since AR5, projections of extratropical cyclones and storm tracks have been examined further, largely confirming previous assessments. In particular, extratropical cyclone precipitation scales with the product of cyclone intensity (as measured by near-surface wind speed) and atmospheric moisture content ( [[#Pfahl--2016|Pfahl and Sprenger, 2016]] ) . Booth et al. (2018) showed that the fraction of rainfall generated by the convection scheme in simulated extratropical cyclones is highly model- and resolution-dependent, which may be a source of uncertainty regarding their precipitation response to anthropogenic forcings. Also, increased moisture availability may increase the maximum intensity of individual storms while reducing the overall frequency as poleward energy transport becomes more efficient.

The role of temperature trends in influencing storm tracks has been further investigated, both in terms of upper tropospheric tropical warming ( [[#Zappa--2017|Zappa and Shepherd, 2017]] ) and lower tropospheric Arctic amplification (J. [[#Wang--2017|]] [[#Wang--2017|]] [[#Wang--2017|Wang et al., 2017]] ), including the direct role of Arctic sea ice loss ( [[#Zappa--2018|Zappa et al., 2018]] ), and the competition between their influences (Shaw et al. , 2016) . Physical linkages between Arctic amplification and changes in the mid-latitudes are uncertain, as discussed in [[IPCC:Wg1:Chapter:Chapter-10|Chapter 10]] (Cross-Chapter Box 10.1). The remote and local SST influence has been further examined by [[#Ciasto--2016|Ciasto et al. (2016)]] , who confirmed sensitivity of the storm tracks to the SST trends generated by the models and suggested that the primary greenhouse gas influence on storm track changes was indirect, acting through the greenhouse gas influence on SSTs. The importance of the stratospheric polar vortex in storm track changes has received more attention ( [[#Zappa--2017|Zappa and Shepherd, 2017]] ; Mindlin et al. , 2020) and the anticipated recovery of the ozone layer further complicates the role of the stratosphere ( [[#Shaw--2016|Shaw et al., 2016]] ; [[#Bracegirdle--2020b|Bracegirdle et al., 2020b]] ).

Biases remain in cyclone locations, intensities, cloud features, and precipitation ( [[#Catto--2016|Catto, 2016]] , [[#Chang--2016|Chang et al., 2016]] ). Uncertainties in projected precipitation changes in many mid-latitude regions can be explained to a large degree by uncertainties in projected storm track or ETC changes. Multiple studies ( [[#Chang--2013|Chang et al., 2013]] ; [[#Zappa--2015|Zappa et al., 2015]] ; [[#Chang--2018|Chang, 2018]] ) have shown strong relationships between model-projected precipitation change in many regions and model-projected change in storm track activity near that regions. While front frequency is well represented, frontal precipitation frequency is too high and the intensity is too low ( [[#Catto--2015|Catto et al., 2015]] ). Some of the bias in storm tracks appears to be related to limitations in model realization of blocking ( [[#Zappa--2014|Zappa et al., 2014]] ). The CMIP6 generation of models has improved representation of storm tracks in both hemispheres ( [[#Bracegirdle--2020a|Bracegirdle et al., 2020a]] ; [[#Harvey--2020|Harvey et al., 2020]] ). Simulation of storm tracks and their associated precipitation generally improve with increasing resolution beyond that used in most current climate models ( Jung et al. , 2006; Michaelis et al. , 2017; Barcikowska et al. , 2018 ). In terms of projections, the decreases in cyclone occurrence over the Mediterranean were replicated in a higher resolution model ( [[#Raible--2018|Raible et al., 2018]] ).

The projected changes in storm tracks and the associated mechanisms have several important implications for water cycle projections. P–E changes in the Mediterranean, California and Chile are directly linked to storm track changes (Zappa et al. , 2020) . Where the storm tracks are robustly projected to shift (SH, North Pacific) or weaken (Mediterranean), understanding the physical causes of the related changes in precipitation helps increase confidence in the projections. Understanding the competing influences provides context for why other regions do not exhibit a consistent signal and cautions against regional projections based on individual models. However, model bias and the need for relatively high resolution to reproduce the relevant dynamics is an important overall limit on confidence in current CMIP6 projections.

In summary, there is the ''high confidence'' that precipitation associated with extratropical storms will increase with global warming in most regions. The SH storm track will ''likely'' shift poleward, the North Pacific storm track ''more likely than not'' will shift poleward, and the North Atlantic storm track is ''unlikely'' to have a simple poleward shift/ display any discernible changes. There is ''low confidence'' in regional storm track changes, although a weakening of the Mediterranean storm track is a robust response of the models.

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===== 8.4.2.8.2 Atmospheric rivers =====

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Atmospheric rivers were not assessed in AR5 but are important in the water cycle as they are linked to extreme rainfall, flooding, and changes in terrestrial water storage including melt and ablation of glaciers and snowpack (Sections 8.2.3). In a warming world, there is ''high confidence'' that thermodynamical increases in atmospheric water vapour ensure that atmospheric rivers will become wetter, hence stronger, and longer-lasting ( [[#Payne--2020|Payne et al., 2020]] ). This is clearly observed in several regional ( [[#Ralph--2011|Ralph and Dettinger, 2011]] ; [[#Lavers--2013|Lavers et al., 2013]] ; [[#Gao--2015|Gao et al., 2015]] ; [[#Payne--2015|Payne and Magnusdottir, 2015]] ; [[#Warner--2015|Warner et al., 2015]] ; [[#Hagos--2016|Hagos et al., 2016]] ; [[#Gershunov--2019|Gershunov et al., 2019]] ) and in one global study (V. [[#Espinoza--2018|]] [[#Espinoza--2018|Espinoza et al., 2018]] ) of atmospheric river activity in CMIP5 model projections. [[#Lavers--2015|Lavers et al. (2015)]] indicate that integrated vapour transport under RCP 8.5 and 4.5 could increase, and consequently this thermodynamic response ( [[#O’Gorman--2015|O’Gorman, 2015]] ) could affect mid-latitude regions where orographic precipitation is important ( [[#Gershunov--2019|Gershunov et al., 2019]] ).

Under continued global warming, more intense moisture transport within atmospheric river events is projected to increase the magnitude of heavy precipitation events on the west coast of the USA ( [[#Ralph--2011|Ralph and Dettinger, 2011]] ; [[#Lavers--2015|Lavers et al., 2015]] ; [[#Warner--2017|Warner and Mass, 2017]] ), in Western Europe ( [[#Lavers--2015|Lavers et al., 2015]] ; [[#Ralph--2016|Ralph et al., 2016]] ; [[#Ramos--2016|Ramos et al., 2016]] ), and in East Asia ( ''very likely'' ) ( [[#Kamae--2019|Kamae et al., 2019]] ). All CMIP5 models analysed agreed under a range of scenarios, except over the Iberian Peninsula ( [[#Ramos--2016|Ramos et al., 2016]] ) where there is only ''low confidence'' in projected changes. [[#Kamae--2019|Kamae et al. (2019)]] reported a 1% increase per °C warming in the frequency of atmospheric rivers affecting East Asia, but this is strongly affected by SST changes. Emerging evidence of possible regional changes due to dynamical factors are uncertain ( [[#Lavers--2013|Lavers et al., 2013]] ; [[#Gao--2015|Gao et al., 2015]] ; [[#Payne--2015|Payne and Magnusdottir, 2015]] ). The frequency, magnitude and duration of atmospheric rivers making landfall along the North American west coast are projected to increase ( [[#Gershunov--2019|Gershunov et al., 2019]] ). In contrast, V. [[#Espinoza--2018|]] [[#Espinoza--2018|Espinoza et al. (2018)]] suggest that the number of atmospheric river events is projected to slightly decrease globally.

In semi-arid regions where atmospheric rivers have historically been important and precipitation is mainly confined to the cold season, the contribution of atmospheric rivers to annual total precipitation may be expected to grow disproportionately. For example, in California decreases in precipitation frequency are projected as a result of fewer non-atmospheric river storms, while the projected increase in heavy and extreme precipitation events are almost entirely a result of increased atmospheric river activity ( [[#Gershunov--2019|Gershunov et al., 2019]] ). Interannual variability in precipitation amounts is projected to increase because of the overall decrease in the frequency of storms but a stronger dependence on extremes ( [[#Polade--2014|Polade et al., 2014]] ), particularly due to atmospheric rivers ( [[#Gershunov--2019|Gershunov et al., 2019]] ), especially where interaction with topography are important ( Polade et al. , 2014; Gershunov et al., 2019 ).

In summary, there is ''high confidence'' that the magnitude and duration of atmospheric rivers are projected to increase in future, leading to increased precipitation. This is projected to increase the intensity of heavy precipitation events on the west coast of the USA and in western Europe ( ''high co'' ''nfidence'' ).

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==== 8.4.2.9 Modes of Climate Variability and Regional Teleconnections ====

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Following on from the assessment of projected changes in modes of climate variability (MoVs) and regional teleconnections ( [[IPCC:Wg1:Chapter:Chapter-4#4.5.3|Section 4.5.3]] ), here we assess their consequences for projected water cycle changes.

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===== 8.4.2.9.1 Tropical modes =====

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CMIP6 projections indicate that the amplitude of ENSO (Annex IV.2.3) variability will not substantially change during the 21st century ( ''high confidence'' ) ( [[IPCC:Wg1:Chapter:Chapter-4#4.4.3.2|Section 4.4.3.2]] ). However, rainfall variability related to ENSO is projected to increase significantly by the second half of the 21st century, regardless of ENSO amplitude ( [[IPCC:Wg1:Chapter:Chapter-4#4.5.3.2|Section 4.5.3.2]] ). Regional precipitation variability associated with ENSO increases due to increases in atmospheric moisture, regardless of changes in ENSO variability itself ( [[#Pendergrass--2017|Pendergrass et al., 2017]] ). In many regions, the magnitude of the projected changes related to ENSO is small compared with historical interannual variability ( [[#Bonfils--2015|Bonfils et al., 2015]] ; [[#Power--2018|Power and Delage, 2018]] ; [[#Perry--2020|Perry et al., 2020]] ). Uncertainties in precipitation projections related to ENSO depend on internal variability associated with the mode ( [[#8.5.2|Section 8.5.2]] ), hence the need to have relatively large ensembles (about 15 members) to adequately estimate uncertainty (Deser et al. , 2018; N. Maher et al. , 2018; C. Sun et al. , 2018; Zheng et al., 2018) .

Even over regions with statistically significant simulated rainfall teleconnections during the historical period, CMIP5 models do not project clear changes ( [[#Perry--2020|Perry et al., 2020]] ). Nonetheless, CMIP5 models that realistically reproduce Indian summer monsoon rainfall indicate a strengthening of its relationship with ENSO in RCP8.5 projections, though the response is not consistent for different varieties of ENSO events ( [[#Roy--2019|Roy et al., 2019]] ). Inconsistent changes in the ENSO–Indian summer monsoon relationship in response to global warming in CMIP5 and CMIP6 models may be related to statistical issues rather than dynamical changes ( [[#Bódai--2020|Bódai et al., 2020]] ; [[#Haszpra--2020|Haszpra et al., 2020]] ). Over East Africa during the boreal spring and summer, ENSO teleconnections are projected to become stronger in the future ( [[#Endris--2019|Endris et al., 2019]] ). Meteorological drought consequences of each strong El Niño are projected to become more severe in the region ( [[#Rifai--2019|Rifai et al., 2019]] ).

Indian Ocean Dipole (IOD, Annex IV.2.4) and Indian Ocean Basin (IOB, Annex IV.2.4) interactions with ENSO are expected to persist in the future ( [[IPCC:Wg1:Chapter:Chapter-4#4.5.3.3|Section 4.5.3.3]] ) but projected changes in the frequency and intensity of events remain uncertain ( [[#Hui--2018|Hui and Zheng, 2018]] ; [[#Endris--2019|Endris et al., 2019]] ; [[#McKenna--2020|McKenna et al., 2020]] ). Climate extremes such as those associated with the extreme positive IOD event of 2019 are expected to occur more frequently under continued global warming ( [[#Cai--2021|Cai et al., 2021]] ). Projected changes in IOD teleconnections are linked to model performance in representing the IOD and its remote influence in the present climate, apparently dominated by a positive IOD event-like mean state (G. [[#Wang--2017|]] [[#Wang--2017|]] [[#Wang--2017|Wang et al., 2017]] ; [[#Huang--2019|Huang et al., 2019]] ). Interactions between the IOD and the Indian Ocean mean state, via atmosphere–ocean feedbacks, can affect the behaviour of the IOD ( [[#Ng--2018|Ng et al., 2018]] ). In the eastern Horn of Africa, OND rainfall is projected to increase because of IOD-ENSO related SST changes in the Indo-Pacific region and associated Walker circulation changes ( [[#Endris--2019|Endris et al., 2019]] ).

Sensitivity studies generally project increases in Madden Julian Oscillation (MJO, Annex IV.2.8) precipitation amplitude in a warmer climate, with increases of up to 14% °C <sup>–1</sup> of warming ( [[#Arnold--2013|Arnold et al., 2013]] , 2015; [[#Caballero--2013|Caballero and Huber, 2013]] ; [[#Liu--2013|Liu and Allan, 2013]] ; [[#Maloney--2013|Maloney and Xie, 2013]] ; [[#Schubert--2013|Schubert et al., 2013]] ; [[#Subramanian--2014|Subramanian et al., 2014]] ; [[#Carlson--2016|Carlson and Caballero, 2016]] ; [[#Pritchard--2016|Pritchard and Yang, 2016]] ; [[#Adames--2017a|Adames et al., 2017a]] ; [[#Wolding--2017|Wolding et al., 2017]] ; [[#Haertel--2018|Haertel, 2018]] ). However, in CMIP5 models with realistic historical MJO behaviour, the precipitation amplitude over the Indo-Pacific warm pool region changes from – 4% to +8% °C <sup>–1</sup> in the RCP8.5 scenario relative to the end of the 20th century ( [[#Bui--2018|Bui and Maloney, 2018]] ; [[#Maloney--2019|Maloney et al., 2019]] ). When simulated MJO precipitation amplitude increases with warming, the leading factor for such change is the intensification of the lower tropospheric vertical moisture gradient, that supports stronger vertical moisture advection per unit diabatic heating ( Arnold et al. , 2015; Adames et al. , 2017a, b; Wolding et al. , 2017 ). In idealized simulations with constant CO <sub>2</sub> forcing with El Niño-like patterns, the MJO activity penetrates farther east into the central and east Pacific with increased warming ( [[#Subramanian--2014|Subramanian et al., 2014]] ; [[#Adames--2017a|Adames et al., 2017a]] ). Increased MJO convective variability in a warmer climate does not reflect into increased ability of the MJO to force the extratropics ( [[#Wolding--2017|Wolding et al., 2017]] ).

In summary, even though there is ''low confidence'' in how the tropical MoVs will change in the future (Sections 4.3.3.2 and 4.5.3.3), their regional hydrological consequences, in terms of precipitation, are projected to intensify ( ''medium confidence'' ). For example, the ENSO influence on precipitation over the Indo–Pacific sector is projected to strengthen and shift eastward ( ''medium confidence'' ). The MJO is projected to intensify in a warmer climate, with increased associated precipitation ( ''medium co'' ''nfidence'' ).

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===== 8.4.2.9.2 Extratropical modes =====

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CMIP6 projections indicate that the Northern Annular Mode (NAM; Annex IV.2.1) is expected to become more positive in winter throughout the 21st century in the SSP3-7.0 and SSP5-8.5 scenarios ( [[IPCC:Wg1:Chapter:Chapter-4#4.5.1|Section 4.5.1]] ). In the near term, the Southern Annular Mode (SAM, Annex IV.2.2) is projected to become less positive than observed during the end of the 20th century during the austral summer in all SSPs scenarios ( [[IPCC:Wg1:Chapter:Chapter-4#4.3.3.1|Section 4.3.3.1]] ).

In the CMIP5 RCP8.5 scenario, increased amplitude and frequency of the North Atlantic Oscillation (NAO, Annex IV.2) during boreal winter (December–January–February, DJF) is associated with higher precipitation in northern Europe and lower precipitation in southern Europe ( [[#Tsanis--2019|Tsanis and Tapoglou, 2019]] ). However, large-ensemble analyses show how the NAO leads to significant uncertainty in future changes of regional climate ( [[#8.5.2|Section 8.5.2]] ). For example, more than a 85% increase in precipitation is projected over northern Europe, western Russia and much of eastern North America, with similar decreasing resulting in drying over north-western Africa and regions adjacent to the Mediterranean Sea ( [[#Deser--2017|Deser et al., 2017]] ).

In the SH, the positive trend projected for the SAM in the CMIP5 RCP8.5 scenario appears to mitigate the wetting in the mid- to high latitudes and the drying over the subtropics, but with strong seasonal dependence ( [[#Lim--2016|Lim et al., 2016]] ). Regional precipitation changes in South America, South Africa, Southern Australia and New Zealand are not well explained by changes in the SAM, but are related to broad-scale changes in north – south temperature gradients associated with enhanced warming of the tropical upper troposphere and strengthening of the stratospheric polar vortex ( [[#Mindlin--2020|Mindlin et al., 2020]] ).

In summary, projected changes in the intensity, frequency and phase of extratropical MoVs (see also Sections 4.3 and 4.5) may amplify regional changes in precipitation and contribute to an increase in their intra-seasonal and interannual variability ( ''medium confidence'' ). Regionally, there are potentially significant precipitation and atmospheric circulation changes associated with changes in extratropical dynamics ( ''low con'' ''fidence'' ).

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