Editing IPCC:AR6/WGI/Chapter-11 (section)

== 11.1 Introduction ==

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=== 11.1.1 Scope of the Chapter ===

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This chapter provides assessments of changes in weather and climate extremes (collectively referred to as extremes) framed in terms of the relevance to the Working Group II (WGII) assessment. It assesses observed changes in extremes, their attribution to causes, and future projections, at three global warming levels: 1.5°C, 2°C, and 4°C. This chapter is also one of the four ‘regional chapters’ of the WGI Report (along with Chapters 10 and 12 and the Atlas). Consequently, while it encompasses assessments of changes in extremes at global and continental scales to provide a large-scale context, it also addresses changes in extremes at regional scales.

Extremes are climatic impact-drivers (Annex VII: Glossary, see [[IPCC:Wg1:Chapter:Chapter-12|Chapter 12]] for a comprehensive assessment). The IPCC risk framework (Chapter 1) articulates clearly that the exposure and vulnerability to climatic impact-drivers, such as extremes, modulate the risk of adverse impacts of these drivers, and that adaptation which reduces exposure and vulnerability will increase resilience, resulting in a reduction in impacts. Nonetheless, changes in extremes lead to changes in impacts as a direct consequence of changes in their magnitude and frequency, and also through their influence on exposure and resilience.

The Special Report on Managing the Risks of Extreme Events and Disasters to Advance Climate Change Adaptation (referred as the SREX report, IPCC, 2012) provided a comprehensive assessment on changes in extremes and how exposure and vulnerability to extremes determine the impacts and likelihood of disasters. [[IPCC:Wg1:Chapter:Chapter-3|Chapter 3]] of that report ( [[#Seneviratne--2012|Seneviratne et al., 2012]] , hereafter also referred to as SREX Chapter 3) assessed physical aspects of extremes, and laid a foundation for the follow-up IPCC assessments. Several chapters of the IPCC Fifth Assessment Report (AR5) (IPCC, 2013) addressed climate extremes with respect to observed changes ( [[#Hartmann--2013|Hartmann et al., 2013]] ), model evaluation ( [[#Flato--2013|Flato et al., 2013]] ), attribution ( [[#Bindoff--2013|Bindoff et al., 2013]] ), and projected long-term changes ( [[#Collins--2013|Collins et al., 2013]] ). Assessments were also provided in the IPCC Special Report on Global Warming of 1.5°C (SR1.5) ( [[#IPCC--2018|IPCC, 2018]] ; [[#Hoegh-Guldberg--2018|Hoegh-Guldberg et al., 2018]] ), on climate change and land (SRCCL; ( [[#IPCC--2019a|IPCC, 2019a]] ), and on oceans and the cryosphere (SROCC; [[#IPCC--2019b|IPCC, 2019b]] ). These assessments are the starting point for the present assessment.

This chapter is structured as follows (Figure 11.1): This section (11.1) provides the general framing and introduction to the chapter, highlighting key aspects that underlie the confidence and uncertainty in the assessment of changes in extremes, and introducing some main elements of the chapter. To provide readers with a quick overview of past and future changes in extremes, a synthesis of global-scale assessments for different types of extremes is included at the end of this section (Tables 11.1 and 11.2). [[#11.2|Section 11.2]] introduces methodological aspects of research on climate extremes. Sections 11.3 to 11.7 assess past changes and their attribution to causes, and projected future changes in extremes, for different types of extremes, including temperature extremes, heavy precipitation and pluvial floods, river floods, droughts, and storms, in separate sections. [[#11.8|Section 11.8]] addresses compound events. [[#11.9|Section 11.9]] summarizes regional assessments of changes in temperature extremes, in precipitation extremes and in droughts by continents in tables. The chapter also includes several boxes and FAQs on more specific topics.

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[[File:559628200b7daeef7fb327d7ea06b439 IPCC_AR6_WGI_Figure_11_1.png]]

'''Figure 11.1 |''' '''Visual guide to Chapter 11.'''

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=== 11.1.2 What Are Extreme Events and How are Their Changes Studied? ===

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Building on the SREX report and AR5, this Report defines an extreme weather event as ‘an event that is rare at a particular place and time of year’, and an extreme climate event as ‘a pattern of extreme weather that persists for some time, such as a season’ (see Glossary). The definitions of ‘rare’ are wide ranging, depending on applications. Some studies consider an event as an extreme if it is unprecedented; other studies consider events that occur several times a year as moderate extreme events. Rarity of an event with a fixed magnitude also changes under human-induced climate change, making events that are unprecedented so far rather probable under present conditions, but unique in the observational record – and thus often considered as ‘surprises’ (see Box 11.2).

Various approaches are used to define extremes. These are generally based on the determination of relative (e.g., 90 <sup>th</sup> percentile) or absolute (e.g., 35°C for a hot day) thresholds above which conditions are considered extremes. Changes in extremes can be examined from two perspectives, either focusing on changes in frequency of given extremes, or on changes in their intensity. These considerations in the definition of extremes are further addressed in [[#11.2.1|Section 11.2.1]] .

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=== 11.1.3 Types of Extremes Assessed in this Chapter ===

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The types of extremes assessed in this chapter include temperature extremes, heavy precipitation and pluvial floods, river floods, droughts, and storms. The drought assessment addresses meteorological droughts, agricultural and ecological droughts, and hydrological droughts (see Glossary). The storms assessment addresses tropical cyclones, extratropical cyclones, and severe convective storms. This chapter also assesses changes in compound events – that is, multivariate or concurrent extreme events – because of their relevance to impacts as well as the emergence of new literature on the subject. Most of the considered extremes were also assessed in SREX and AR5. Compound events were not assessed in depth in past IPCC reports (SREX Chapter 3; [[#11.8|Section 11.8]] of this Report). Marine-related extremes such as marine heatwaves and extreme sea level, are assessed in [[IPCC:Wg1:Chapter:Chapter-9#9.6.4|Section 9.6.4]] and Box 9.2 of this Report.

Extremes and related phenomena are of various spatial and temporal scales. Tornadoes have a spatial scale as small as less than 100 metres and a temporal scale as short as a few minutes. In contrast, a drought can last for multiple years, affecting vast regions. The level of complexity of the involved processes differs from one type of extreme to another, affecting our capability to detect, attribute and project changes in weather and climate extremes. Temperature and precipitation extremes studied in the literature are often based on extremes derived from daily values. Studies of events on longer time scales for temperature or precipitation, or on sub-daily extremes, are scarcer, which generally limits the assessment for such events. Nevertheless, extremes on time scales different from daily are assessed for temperature extremes and heavy precipitation, when possible (Sections 11.3 and 11.4). Droughts and tropical cyclones are treated as phenomena in general in the assessment, not limited by their extreme forms, because these phenomena are relevant to impacts (Sections 11.6 and 11.7). Both precipitation and wind extremes associated with storms are considered.

Multiple concomitant extremes can lead to stronger impacts than those resulting from the same extremes had they happened in isolation. For this reason, the occurrence of multiple extremes that are multivariate and/or concurrent and/or happening in succession, also called ‘compound events’ (SREX Chapter 3), are assessed in this chapter based on emerging literature on this topic ( [[#11.8|Section 11.8]] ). Box 11.2 also provides an assessment on low-likelihood, high-impact scenarios associated with extremes.

The assessment of projected future changes in extremes is presented as function of different global warming levels ( [[#11.2.4|Section 11.2.4]] and Cross-Chapter Box 11.1). This provides traceability and comparison to the SR1.5 assessment ( [[#Hoegh-Guldberg--2018|Hoegh-Guldberg et al., 2018]] , hereafter referred to as SR1.5 Chapter 3). Also, this is useful for decision makers as actionable information, as much of the mitigation policy discussion and adaptation planning can be tied to the level of global warming. For example, regional changes in extremes, and thus their impacts, can be linked to global mitigation efforts. There is also the advantage of separating uncertainty in future projections due to regional responses as a function of global warming levels from other factors such as differences in global climate sensitivity and emissions scenarios (Cross-Chapter Box 11.1). Information is also provided on the translation between information provided at global warming levels and for single emissions scenarios (Cross-Chapter Box 11.1). This facilitates easier comparison with the AR5 assessment and with some analyses provided in other chapters as function of emissions scenarios.

A global-scale synthesis of this chapter’s assessments is provided in [[#11.1.7|Section 11.1.7]] . In particular, Tables 11.1 and 11.2 provide a synthesis for observed and attributed changes, and projected changes in extremes, respectively, at different global warming levels (1.5°C, 2°C, and 4°C). Tables on regional-scale assessments for changes in temperature extremes, heavy precipitation and droughts, are provided in [[#11.9|Section 11.9]] .

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=== 11.1.4 Effects of Greenhouse Gas and Other External Forcings on Extremes ===

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The SREX, AR5, and SR1.5 assessed that there is evidence from observations that some extremes have changed since the mid-20th century, that some of the changes are a result of anthropogenic influences, and that some observed changes are projected to continue into the future. Additionally, other changes are projected to emerge from natural climate variability under enhanced global warming (SREX Chapter 3; AR5 Chapter 10).

At the global scale, and also at the regional scale to some extent, many of the changes in extremes are a direct consequence of enhanced radiative forcing, and the associated global warming and/or resultant increase in the water-holding capacity of the atmosphere, as well as changes in vertical stability and meridional temperature gradients that affect climate dynamics (see Box 11.1). Widespread observed and projected increases in the intensity and frequency of hot extremes, together with decreases in the intensity and frequency of cold extremes, are consistent with global and regional warming ( [[#11.3|Section 11.3]] and Figure 11.2). Extreme temperatures on land tend to increase more than the global mean temperature (Figure 11.2), due in large part to the land–sea warming contrast, and additionally to regional feedbacks in some regions ( [[#11.1.6|Section 11.1.6]] ). Increases in the intensity of temperature extremes scale robustly, and in general linearly, with global warming across different geographical regions in projections up to 2100, with minimal dependence on emissions scenarios ( [[#11.2.4|Section 11.2.4]] , Figure 11.3,and Cross-Chapter Box 11.1; [[#Seneviratne--2016|Seneviratne et al., 2016]] ; [[#Wartenburger--2017|Wartenburger et al., 2017]] ; [[#Kharin--2018|Kharin et al., 2018]] ). The frequency of hot temperature extremes (see Figure 11.6), the number of heatwave days and the length of heatwave seasons in various regions also scale well, but nonlinearly (because of threshold effects, [[#11.2.1|Section 11.2.1]] ), with global mean temperatures ( [[#Wartenburger--2017|Wartenburger et al., 2017]] ; Y. [[#Sun--2018a|]] [[#Sun--2018|Sun et al., 2018]] a ).

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[[File:0bcabfcb85203ec3ac5629fcc018677d IPCC_AR6_WGI_Figure_11_2.png]]

'''Figure 11.2 |''' '''Time series of observed temperature anomalies for global average annual mean temperature (black), land average annual mean temperature (green), land average annual hottest daily maximum temperature (TXx, purple), and land average annual coldest daily minimum temperature (TNn, blue).''' Global and land mean temperature anomalies are relative to their 1850–1900 means and are based on the multi-product mean annual time series assessed in [[IPCC:Wg1:Chapter:Chapter-2#2.3.1.1.3|Section 2.3.1.1.3]] (see text for references). TXx and TNn anomalies are relative to their respective 1961–1990 means and are based on the HadEX3 dataset ( [[#Dunn--2020|Dunn et al., 2020]] ) using values for grid boxes with at least 90% temporal completeness over 1961–2018. Further details on data sources and processing are available in the chapter data table (Table 11.SM.9).

Changes in annual maximum one-day precipitation (Rx1day) are proportional to mean global surface temperature changes, at about 7% increase per 1°C of warming, that is, following the Clausius–Clapeyron relation (Box 11.1), both in observations ( [[#Westra--2013|Westra et al., 2013]] ) and in future projections ( [[#Kharin--2013|Kharin et al., 2013]] ) at the global scale. Extreme short-duration precipitation in North America also scales with global surface temperature ( [[#Prein--2016b|Prein et al., 2016b]] ; [[#Li--2019a|]] [[#Li--2019|]] [[#Li--2019|]] [[#Li--2019|C. Li et al., 2019]] a ). At the local and regional scales, changes in extremes are also strongly modulated and controlled by regional forcings and feedback mechanisms ( [[#11.1.6|Section 11.1.6]] ), whereby some regional forcings, for example, associated with changes in land cover and land use or aerosol emissions, can have non-local or some (non-homogeneous) global-scale effects. In general, there is ''high confidence'' in changes in extremes due to global-scale thermodynamic processes (i.e., global warming, mean moistening of the air) as the processes are well understood, while the confidence in those related to dynamic processes or regional and local forcing, including regional and local thermodynamic processes, is much lower due to multiple factors (see the following subsection and Box 11.1).

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[[File:8e0a33b4b21ac32fe7915a86e48b0251 IPCC_AR6_WGI_Figure_11_3.png]]

'''Figure 11.3 |''' '''Regional mean changes in annual hottest daily maximum temperature (TXx) for AR6 land regions and the global land area (except Antarctica), against changes in global mean surface air temperature (GSAT) as simulated by Coupled Model Intercomparison Project Phase 6 (CMIP6) models under different Shared Socio-economic Pathway (SSP) forci''' '''ng scena''' '''ri''' '''os, SSP1''' '''-1.9''' ''', SSP1''' '''-2.6''' ''', SSP2''' '''-4.5,''' '''SSP3-7.''' ''0, and SSP5-8.5.'' Changes in TXx and GSAT are relative to the 1850–1900 baseline, and changes in GSAT are expressed as global warming level. ''(a)'' Individual models from the CMIP6 ensemble (grey), the multi-model median under three selected SSPs (colours), and the multi-model median (black); ''(b) to (l)'' Multi-model median for the pooled data for individual AR6 regions. Numbers in parentheses indicate the linear scaling between regional TXx and GSAT. The black line indicates the 1:1 reference scaling between TXx and GSAT. See Atlas.1.3.2 for the definition of regions. Changes in TXx are also displayed in the Interactive Atlas. For details on the methods, see Supplementary Material 11.SM.2.

Since AR5, the attribution of extreme weather events, or the investigation of changes in the frequency and/or magnitude of individual and local- and regional-scale extreme weather events due to various drivers ( [[#11.2.3|Section 11.2.3]] and Cross-Working Group Box 1.1) has provided evidence that greenhouse gases and other external forcings have affected individual extreme weather events. The events that have been studied are geographically uneven. For example, extreme rainfall events in the UK ( [[#Schaller--2016|Schaller et al., 2016]] ; [[#Vautard--2016|Vautard et al., 2016]] ; [[#Otto--2018b|Otto et al., 2018b]] ) or heatwaves in Australia ( [[#King--2014|King et al., 2014]] ; [[#Perkins-Kirkpatrick--2016|Perkins-Kirkpatrick et al., 2016]] ; [[#Lewis--2017b|Lewis et al., 2017b]] ) have spurred more studies than other events. Many highly impactful extreme weather events have not been studied in the event attribution framework. Studies in the developing world are also generally lacking. This is due to various reasons ( [[#11.2|Section 11.2]] ) including lack of observational data, lack of reliable climate models and other problems ( [[#Otto--2020|Otto et al., 2020]] ). While the events that have been studied are not representative of all extreme events that occurred, and results from these studies may also be subject to selection bias, the large number of event attribution studies provide evidence that changes in the properties of these local and individual events are in line with expected consequences of human influence on the climate and can be attributed to external drivers ( [[#11.9|Section 11.9]] ). Figure 11.4 summarizes assessments of observed changes in temperature extremes, in heavy precipitation and in droughts, and their attribution in a map form.

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[[File:04c90b94b4a0466285f1e5a28d9446a8 IPCC_AR6_WGI_Figure_11_4.png]]

'''Figure 11.4 |''' '''Overview of observed changes for cold, hot, and wet extremes and their potential human contribution.''' Shown are the direction of change and the confidence in: 1) the observed changes in cold and hot as well as wet extremes across the world; and 2) whether human-induced climate change contributed to causing these changes (attribution). In each region changes in extremes are indicated by colour (orange – increase in the type of extreme; blue – decrease; both colours – changes of opposing direction within the region, with the signal depending on the exact event definition; grey – there are no changes observed; and no fill – the data/evidence is too sparse to make an assessment). The squares and dots next to the symbol indicate the level of confidence for observing the trend and the human contribution, respectively. The more black dots/squares, the higher the level of confidence. The information on this figure is based on regional assessment of the literature on observed trends, detection and attribution and event attribution in [[#11.9|Section 11.9]] .

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Box 11.1 | Thermodynamic and Dynamic Changes in Extremes Across Scales

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Changes in weather and climate extremes are determined by local exchanges in heat, moisture, and other related quantities (thermodynamic changes) and those associated with atmospheric and oceanic motions (dynamic changes). While thermodynamic and dynamic processes are interconnected, considering them separately helps to disentangle the roles of different processes contributing to changes in climate extremes (e.g., [[#Shepherd--2014|Shepherd, 2014]] ).

'''Temperature extremes'''

An increase in the concentration of greenhouse gases in the atmosphere leads to the warming of tropospheric air and the Earth’s surface. This direct thermodynamic effect leads to warmer temperatures everywhere, with an increase in the frequency and intensity of warm extremes, and a decrease in the frequency and intensity of cold extremes. The initial increase in temperature leads to other thermodynamic responses and feedbacks affecting the atmosphere and the surface. These include an increase in the water vapour content of the atmosphere (water vapour feedback, see [[IPCC:Wg1:Chapter:Chapter-7#7.4.2.2|Section 7.4.2.2]] ) and a change in the vertical profile of temperature (lapse rate feedback, see [[IPCC:Wg1:Chapter:Chapter-7#7.4.2.2|Section 7.4.2.2]] ). While the water vapour feedback always amplifies the initial temperature increases (positive feedback), the lapse rate feedback amplifies near-surface temperature increases (positive feedback) in mid- and high latitudes but reduces temperature increases (negative feedback) in tropical regions ( [[#Pithan--2014|Pithan and Mauritsen, 2014]] ).

Thermodynamic responses and feedbacks also occur through surface processes. For instance, observations and model simulations show that temperature increases, including extreme temperatures, are amplified in areas where seasonal snow cover is reduced due to decreases in surface albedo (see [[#11.3.1|Section 11.3.1]] ). In some mid-latitude areas, temperature increases are amplified by the higher atmospheric evaporative demand ( [[#Fu--2014|Fu and Feng, 2014]] ; [[#Vicente-Serrano--2020a|Vicente-Serrano et al., 2020a]] ) that results in a drying of soils in some regions ( [[#11.6|Section 11.6]] ), leading to increased sensible heat fluxes (soil-moisture – temperature feedback, see Sections 11.1.6 and 11.3.1 for more background). Other thermodynamic feedback processes include changes in the water-use efficiency of plants under enhanced atmospheric carbon dioxide (CO <sub>2</sub> ) concentrations that can reduce the overall transpiration, and thus also enhance temperature in projections (Sections 8.2.3.3, 11.1.6, 11.3 and 11.6).

Changes in the spatial distribution of temperatures can also affect temperature extremes by modifying the characteristics of weather patterns (e.g., [[#Suarez-Gutierrez--2020a|Suarez-Gutierrez et al., 2020a]] ). For example, a robust thermodynamic effect of polar amplification is a weakened north-south temperature gradient, which amplifies the warming of cold extremes in the Northern Hemisphere mid- and high latitudes because of the reduction of cold air advection ( [[#Holmes--2015|Holmes et al., 2015]] ; [[#Schneider--2015|Schneider et al., 2015]] ; [[#Gross--2020|Gross et al., 2020]] ). Much less robust is the dynamic effect of polar amplification ( [[IPCC:Wg1:Chapter:Chapter-7#7.4.4.1|Section 7.4.4.1]] ) and the reduced low-altitude meridional temperature gradient that has been linked to an increase in the persistence of weather patterns (e.g., heatwaves) and subsequent increases in temperature extremes (Cross-Chapter Box 10.1; [[#Francis--2012|Francis and Vavrus, 2012]] ; Coumou et al. , 2015, 2018; Mann et al., 2017).

'''Precipitation extremes'''

Changes in temperature also control changes in water vapour through increases in evaporation and in the water-holding capacity of the atmosphere ( [[IPCC:Wg1:Chapter:Chapter-8#8.2.1|Section 8.2.1]] ). At the global scale, column-integrated water vapour content increases roughly following the Clausius–Clapeyron (C-C) relation, with an increase of approximately 7% per 1°C of global-mean surface warming ( [[IPCC:Wg1:Chapter:Chapter-8#8.2.1|Section 8.2.1]] ). Nonetheless, at regional scales, water vapour increases differ from this C-C rate due to several reasons ( [[IPCC:Wg1:Chapter:Chapter-8#8.2.2|Section 8.2.2]] ), including a change in weather regimes and limitations in moisture transport from the ocean, which warms more slowly than land ( [[#Byrne--2018|Byrne and O’Gorman, 2018]] ). Observational studies ( [[#Fischer--2016|Fischer and Knutti, 2016]] ; [[#Sun--2021|Sun et al., 2021]] ) have shown that the observed rate of increased precipitation extremes is similar to the C-C rate at the global scale. Climate model projections show that the increase in water vapour leads to robust increases in precipitation extremes everywhere, with a magnitude that varies between 4% and 8% per 1°C of surface warming (thermodynamic contribution, Box 11.1, Figure 1b). At regional scales, climate models show that the dynamic contribution (Box 11.1, Figure 1c) can be substantial and strongly modify the projected rate of change of extreme precipitation (Box 11.1, Figure 1a) with large regions in the subtropics showing robust reductions and other areas (e.g., equatorial Pacific) showing robust amplifications (Box 11.1, Figure 1c). However, the dynamic contributions show large differences across models and are more uncertain than thermodynamic contributions (Box 11.1, Figure 1c; [[#Shepherd--2014|Shepherd, 2014]] ; [[#Trenberth--2015|Trenberth et al., 2015]] ; [[#Pfahl--2017|Pfahl et al., 2017]] ).

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[[File:335c666394fc1f6cf3fa86f0e95dd4d4 IPCC_AR6_WGI_Box_11_1_Figure_1.png]]

'''Box 11.1, Figure 1:'''

'''Multi-model Coupled Model Intercomparison Project Phase 5 (CMIP5) mean fractional changes (in % per degree of warming). (a)''' changes in annual maximum precipitation (Rx1day); (b) changes in Rx1day due to the thermodynamic contribution; and (c) changes in Rx1day due to the dynamic contribution estimated as the difference between the total changes and the thermodynamic contribution. Changes were derived from a linear regression for the period 1950–2100. Uncertainty is represented using the simple approach: no overlay indicates regions with high model agreement, where ≥80% of models (n=22) agree on the sign of change; diagonal lines indicate regions with low model agreement, where &lt;80% of models agree on the sign of change. For more information on the simple approach, please refer to the Cross-Chapter Box ( [[IPCC:Wg1:Chapter:Atlas|Atlas]] 1. A detailed description of the estimation of dynamic and thermodynamic contributions is given in Pfahl et al. (2017). Figure adapted from Pfahl et al. (2017), originally published in ''Nature Climate Change/Springer Nature.'' Further details on data sources and processing are available in the chapter data table (Table 11.SM.9).

Dynamic contributions can occur in response to changes in the vertical and horizontal distribution of temperature (thermodynamics) and can affect the frequency and intensity of synoptic and subsynoptic phenomena, including tropical cyclones, extratropical cyclones, fronts, mesoscale-convective systems and thunderstorms. For example, the poleward shift and strengthening of the Southern Hemisphere mid-latitude storm tracks ( [[IPCC:Wg1:Chapter:Chapter-4#4.5.1|Section 4.5.1]] ) can modify the frequency or intensity of extreme precipitation. However, the precise way in which dynamic changes will affect precipitation extremes is unclear due to several competing effects ( [[#Shaw--2016|Shaw et al., 2016]] ; [[#Allan--2020|Allan et al., 2020]] ).

Box 11.1

Extreme precipitation can also be enhanced by dynamic responses and feedbacks occurring within storms that result from the extra latent heat released from the thermodynamic increases in moisture ( [[#Lackmann--2013|Lackmann, 2013]] ; Willison et al. , 2013; Marciano et al. , 2015; Nie et al. , 2018; [[#Mizuta--2020|Mizuta and Endo, 2020]] ). The extra latent heat released within storms has been shown to increase precipitation extremes by strengthening convective updrafts and the intensity of the cyclonic circulation (e.g., [[#Molnar--2015|Molnar et al., 2015]] ; [[#Nie--2018|Nie et al., 2018]] ), although weakening effects have also been found in mid-latitude cyclones (e.g., [[#Kirshbaum--2017|Kirshbaum et al., 2017]] ). Additionally, the increase in latent heat can also suppress convection at larger scales due to atmospheric stabilization ( [[#Nie--2018|Nie et al., 2018]] ; [[#Tandon--2018|Tandon et al., 2018]] ; [[#Kendon--2019|Kendon et al., 2019]] ). As these dynamic effects result from feedback processes within storms where convective processes are crucial, their proper representation might require improving the horizontal/vertical resolution, the formulation of parametrizations, or both, in current climate models (i.e., Kendon et al. , 2014; Westra et al. , 2014; Ban et al. , 2015; Meredith et al. , 2015; Prein et al. , 2015; Nie et al., 2018).

'''Droughts'''

Droughts are also affected by thermodynamic and dynamic processes (Sections 8.2.3.3 and 11.6). Thermodynamic processes affect droughts by increasing atmospheric evaporative demand ( [[#Martin--2018|Martin, 2018]] ; [[#Gebremeskel%20Haile--2020|Gebremeskel Haile et al., 2020]] ; [[#Vicente-Serrano--2020a|Vicente-Serrano et al., 2020a]] ) through changes in air temperature, radiation, wind speed, and relative humidity. Dynamic processes affect droughts through changes in the occurrence, duration and intensity of weather anomalies, which are related to precipitation and the amount of sunlight ( [[#11.6|Section 11.6]] ). While atmospheric evaporative demand increases with warming, regional changes in aridity are affected by increasing land–ocean warming contrast, vegetation feedbacks and responses to rising CO <sub>2</sub> concentrations, and dynamic shifts in the location of the wet and dry parts of the atmospheric circulation in response to climate change, as well as internal variability ( [[#Byrne--2015|Byrne and O’Gorman, 2015]] ; [[#Kumar--2015|Kumar et al., 2015]] ; [[#Allan--2020|Allan et al., 2020]] ).

In summary, both thermodynamic and dynamic processes are involved in the changes of extremes in response to warming. Anthropogenic forcing (e.g., increases in greenhouse gas concentrations) directly affects thermodynamic variables, including overall increases in high temperatures and atmospheric evaporative demand, and regional changes in atmospheric moisture, which intensify heatwaves, droughts and heavy precipitation events when they occur ( ''high confidence'' ). Dynamic processes are often indirect responses to thermodynamic changes, are strongly affected by internal climate variability, and are also less well understood. As such, there is ''low confidence'' in how dynamic changes affect the location and magnitude of extreme events in a warming climate.

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=== 11.1.5 Effects of Large-scale Circulation on Changes in Extremes ===

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Atmospheric large-scale circulation patterns and associated atmospheric dynamics are important determinants of the regional climate (Chapter 10). As a result, they are also important to the magnitude, frequency, and duration of extremes (Box 11.4). Aspects of changes in large-scale circulation patterns are assessed in Chapters 2, 3, 4 and 8, and representative atmospheric and oceanic modes are described in Annex IV. This subsection provides some general concepts, through a couple of examples, on why the uncertainty in the response of large-scale circulation patterns to external forcing can cascade to uncertainty in the response of extremes to external forcings. Details for specific types of extremes are covered in the relevant subsections. For example, the occurrence of the El Niño–Southern Oscillation (ENSO) influences precipitation regimes in many areas, favouring droughts in some regions and heavy rains in others (Box 11.4). The extent and strength of the Hadley circulation influences regions where tropical and extratropical cyclones occur, with important consequences for the characteristics of extreme precipitation, drought, and winds ( [[#11.7|Section 11.7]] ). Changes in circulation patterns associated with land–ocean heat contrast, which affect the monsoon circulations ( [[IPCC:Wg1:Chapter:Chapter-8#8.4.2.4|Section 8.4.2.4]] ), lead to heavy precipitation along the coastal regions in East Asia ( [[#Freychet--2015|Freychet et al., 2015]] ). As a result, changes in the spatial and/or temporal variability of the atmospheric circulation in response to warming affect characteristics of weather systems such as tropical cyclones ( [[#Sharmila--2018|Sharmila and Walsh, 2018]] ), storm tracks ( [[#Shaw--2016|Shaw et al., 2016]] ), and atmospheric rivers ( [[#11.7|Section 11.7]] ; [[#Waliser--2017|Waliser and Guan, 2017]] ). Changes in weather systems come with changes in the frequency and intensity of extreme winds, extreme temperatures, and extreme precipitation, on the backdrop of thermodynamic responses of extremes to warming (Box 11.1). Floods are also affected by large-scale circulation modes, including ENSO, the North Atlantic Oscillation (NAO), the Atlantic Multi-decadal Variability (AMV), and the Pacific Decadal Variability (PDV) ( [[#Kundzewicz--2018|Kundzewicz et al., 2018]] ; Annex IV). Aerosol forcing, through changes in patterns of sea surface temperatures (SSTs), also affects circulation patterns and tropical cyclone activities ( [[#Takahashi--2017|Takahashi et al., 2017]] ).

In general, changes in atmospheric large-scale circulation due to external forcing are uncertain, but there are some robust changes (Sections 2.3.1.4 and 8.2.2.2). Among them, there has been a ''very likely'' widening of the Hadley circulation since the 1980s and the extratropical jets and cyclone tracks have ''likely'' been shifting poleward since the 1980s ( [[IPCC:Wg1:Chapter:Chapter-2#2.3.1.4|Section 2.3.1.4]] ). The poleward expansion affects drought occurrence in some regions ( [[#11.6|Section 11.6]] ), and results in poleward shifts of tropical cyclones and storm tracks (Sections 11.7.1 and 11.7.2). Although it is ''very likely'' that the amplitude of ENSO variability will not robustly change over the 21st century ( [[IPCC:Wg1:Chapter:Chapter-4#4.3.3.2|Section 4.3.3.2]] ), the frequency of extreme ENSO events (Box 11.4), defined by precipitation threshold, is projected to increase with global warming (Section 6.5 of SROCC). This would have implications for projected changes in extreme events affected by ENSO, including droughts over wide areas ( [[#11.6|Section 11.6]] ; Box 11.4) and tropical cyclones ( [[#11.7.1|Section 11.7.1]] ). A case study is provided for extreme ENSO events in 2015–2016 in Box 11.4 to highlight the influence of ENSO on extremes.

In summary, large-scale atmospheric circulation patterns are important drivers for local and regional extremes. There is overall ''low confidence'' about future changes in the magnitude, frequency, and spatial distribution of these patterns, which contributes to uncertainty in projected responses of extremes, especially in the near term.

<div id="11.1.6" class="h2-container"></div>

<span id="effects-of-regional-scale-processes-and-forcings-and-feedbacks-on-changes-in-extremes"></span>
=== 11.1.6 Effects of Regional-scale Processes and Forcings and Feedbacks on Changes in Extremes ===

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At the local and regional scales, changes in extremes are strongly modulated by local and regional feedbacks (SRCCL, [[#Jia--2019|Jia et al., 2019]] ; [[#Seneviratne--2013|Seneviratne et al., 2013]] ; [[#Miralles--2014a|Miralles et al., 2014a]] ; [[#Lorenz--2016|Lorenz et al., 2016]] ; [[#Vogel--2017|Vogel et al., 2017]] ), changes in large-scale circulation patterns ( [[#11.1.5|Section 11.1.5]] ), and regional forcings such as changes in land use or aerosol concentrations (Chapters 3 and 7; [[#Findell--2017|Findell et al., 2017]] ; [[#Hirsch--2017|Hirsch et al., 2017]] , 2018; [[#Thiery--2017|Thiery et al., 2017]] ; Z. [[#Wang--2017|]] [[#Wang--2017|Wang et al., 2017]] b). In some cases, such responses may also include non-local effects (e.g., [[#de%20Vrese--2016|de Vrese et al., 2016]] ; [[#Persad--2018|Persad and Caldeira, 2018]] ; [[#Miralles--2019|Miralles et al., 2019]] ; [[#Schumacher--2019|Schumacher et al., 2019]] ). Regional-scale forcing and feedbacks often affect temperature distributions asymmetrically, with generally higher effects for the hottest percentiles ( [[#11.3|Section 11.3]] ).

Land use can affect regional extremes, in particular hot extremes, in several ways ( ''high confidence'' ). This includes effects of land management (e.g., cropland intensification, irrigation, double cropping) as well as of land cover changes (deforestation; Sections 11.3.2 and 11.6). Some of these processes are not well represented (e.g., effects of forest cover on diurnal temperature cycle) or not integrated (e.g., irrigation) in climate models (Sections 11.3.2 and 11.3.3). Overall, the effects of land-use forcing may be particularly relevant in the context of low-emissions scenarios, which include large land-use modifications, for instance those associated with the expansion of biofuels, bioenergy with carbon capture and storage, or re-/afforestation to ensure negative emissions, as well as with the expansion of food production (e.g., SR1.5, Chapter 3; Cross-Chapter Box 5.1 in this Report; [[#van%20Vuuren--2011|van Vuuren et al., 2011]] ; [[#Hirsch--2018|Hirsch et al., 2018]] ). There are also effects on the water cycle through freshwater use ( [[#11.6|Section 11.6]] and Cross-Chapter Box 5.1).

Aerosol forcing also has a strong regional footprint associated with regional emissions, which affects temperature and precipitation extremes ( ''high confidence'' ) (Sections 11.3 and 11.4). From around the 1950s to 1980s, enhanced aerosol loadings led to regional cooling due to decreased global solar radiation (‘global dimming’) which was followed by a phase of ‘global brightening’ due to a reduction in aerosol loadings (Chapters 3 and 7; [[#Wild--2005|Wild et al., 2005]] ). [[#King--2016b|King et al. (2016b)]] show that aerosol-induced cooling delayed the timing of a significant human contribution to record-breaking heat extremes in some regions. However, the decreased aerosol loading since the 1990s has led to an accelerated warming of hot extremes in some regions. Based on Earth system model (ESM) simulations, [[#Dong--2017|Dong et al. (2017)]] suggest that a substantial fraction of the warming of the annual hottest days in Western Europe since the mid-1990s has been due to decreases in aerosol concentrations in the region. [[#Dong--2016b|Dong et al. (2016b)]] also identify non-local effects of decreases in aerosol concentrations in Western Europe, which they estimate played a dominant role in the warming of the hottest daytime temperatures in north-east Asia since the mid-1990s, via induced coupled atmosphere–land surface and cloud feedbacks, rather than a direct impact of anthropogenic aerosol changes on cloud condensation nuclei.

In addition to regional forcings, regional feedback mechanisms can also substantially affect extremes ( ''high confidence'' ) (Sections 11.3, 11.4 and 11.6). In particular, soil moisture feedbacks play an important role for extremes in several mid-latitude regions, leading to a marked additional warming of hot extremes compared to mean global warming ( [[#Seneviratne--2016|Seneviratne et al., 2016]] ; [[#Bathiany--2018|Bathiany et al., 2018]] ; [[#Miralles--2019|Miralles et al., 2019]] ), which is superimposed on the known land–sea contrast in mean warming ( [[#Vogel--2017|Vogel et al., 2017]] ). Soil moisture–atmosphere feedbacks also affect drought development ( [[#11.6|Section 11.6]] ). Additionally, effects of land surface conditions on circulation patterns have also been reported ( [[#Koster--2016|Koster et al., 2016]] ; [[#Sato--2019|Sato and Nakamura, 2019]] ). These regional feedbacks are also associated with substantial spread in models ( [[#11.3|Section 11.3]] ), and contribute to the identified higher spread of regional projections of temperature extremes as a function of global warming, compared with the spread resulting from the differences in projected global warming (global transient climate responses) in climate models ( [[#Seneviratne--2020|Seneviratne and Hauser, 2020]] ). In addition, there are also feedbacks between soil moisture content and precipitation occurrence, generally characterized by negative spatial feedbacks and positive local feedbacks (Taylor et al., 2012; [[#Guillod--2015|Guillod et al., 2015]] ). Climate model projections suggest that these feedbacks are relevant for projected changes in heavy precipitation ( [[#Seneviratne--2013|Seneviratne et al., 2013]] ). However, there is evidence that climate models do not capture the correct sign of the soil moisture–precipitation feedbacks in several regions, in particular spatially, and/or in some cases also temporally (Taylor et al., 2012; [[#Moon--2019|Moon et al., 2019]] ). In the Northern Hemisphere high latitudes, the snow- and ice-albedo feedback, along with other factors, is projected to largely amplify temperature increases (e.g., [[#Pithan--2014|Pithan and Mauritsen, 2014]] ), although the effect on temperature extremes is still unclear. It also remains unclear whether snow-albedo feedbacks in mountainous regions might have an effect on temperature and precipitation extremes (e.g., [[#Gobiet--2014|Gobiet et al., 2014]] ). However, these feedbacks play an important role in projected changes in high-latitude warming ( [[#Hall--2006|Hall and Qu, 2006]] ), and, in particular, in changes in cold extremes in these regions ( [[#11.3|Section 11.3]] ).

Finally, extreme events may also regionally amplify one another. For example, this is the case for heatwaves and droughts, with high temperatures and stronger radiative forcing leading to drying tendencies on land due to increased evapotranspiration ( [[#11.6|Section 11.6]] ), and drier soils then inducing decreased evapotranspiration and higher sensible heat flux and hot temperatures (Box 11.1, [[#11.8|Section 11.8]] ; [[#Seneviratne--2013|Seneviratne et al., 2013]] ; [[#Miralles--2014a|Miralles et al., 2014a]] ; [[#Vogel--2017|Vogel et al., 2017]] ; [[#Zscheischler--2017|Zscheischler and Seneviratne, 2017]] ; S. [[#Zhou--2019|]] [[#Zhou--2019|Zhou et al., 2019]] ; [[#Kong--2020|Kong et al., 2020]] ).

In summary, regional forcings and feedbacks – in particular those associated with land use and aerosol forcings – and soil-moisture–temperature, soil moisture–precipitation, and snow/ice–albedo–temperature feedbacks, play an important role in modulating regional changes in extremes. These can also lead to a higher warming of extreme temperatures compared to mean temperature ( ''high confidence'' ), and possibly cooling in some regions ( ''medium confidence'' ). However, there is only ''medium confidence'' in the representation of the associated processes in state-of-the-art ESMs.

<div id="11.1.7" class="h2-container"></div>

<span id="global-scale-synthesis"></span>
=== 11.1.7 Global-scale Synthesis ===

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Tables 11.1 and 11.2 provide a synthesis for observed and attributed changes in extremes, and projected changes in extremes, respectively, at different levels of global warming. This synthesis assessment focuses on the assessed range of observed and projected changes. In this chapter, the assessed ''likely'' range in a projection typically corresponds to the 90% range of the multi-model ensemble spread to take into account other sources of uncertainty, unless stated otherwise. Some low-likelihood, high-impact scenarios that can be of high relevance are addressed in Box 11.2.

Building on the assessments from Tables 11.1 and 11.2, Figure 11.5 provides a synthesis on the level of confidence in the attribution and projection of changes in extremes. In the case where the signal in the observations is still relatively weak but the physical processes underlying the changes in extremes in response to human forcing are well understood, confidence in the projections would be higher than in the attribution because of strengthening in the signal with warming. But, when the observed signal is already strong and when observational evidence is consistent with model simulated responses, confidence in the projection may be lower than that in attribution if certain physical processes could be expected to behave differently in a much warmer world and under much higher greenhouse gas forcing, and in particular if such a behaviour is poorly understood.

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[[File:a5ba7c3af5ed593cda01d747d3b2d896 IPCC_AR6_WGI_Figure_11_5.png]]

'''Figure 11.5 |''' '''confidence and likelihood of past changes and projected future changes at 2°C of global warming on the global scale.''' The information in this figure is based on Tables 11.1 and 11.2.

Further synthesis for regional assessments are provided in Figure 11.4 (event attribution), Figure 11.6 (projected change in hot temperature extremes) and Figure 11.7 (projected changes in precipitation extremes). A synthesis on regional assessments for observed, attributed and projected changes in extremes is provided in [[#11.9|Section 11.9]] for all AR6 reference regions (see [[IPCC:Wg1:Chapter:Chapter-1#1.4.5|Section 1.4.5]] and Figures 1.18 and Atlas.2 for definitions of AR6 regions).

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[[File:86a62770ec72d8bc8bed568c837492f8 IPCC_AR6_WGI_Figure_11_6.png]]

'''Figure 11.6 |''' '''Projected changes in the frequency of extreme temperature events under 1°C, 1.5°C, 2°C, 3°C, and 4°C global warming levels relative to the 18''' ''50–1900 baseline.'' Extreme temperatures are defined as the maximum daily temperatures that were exceeded on average once during a 10-year period (10-year event, blue) and once during a 50-year period (50-year event, orange) during the 1850–1900 base period. Results are shown for the global land area and the AR6 regions. For each box plot, the horizontal line and the box represent the median and central 66% uncertainty range, respectively, of the frequency changes across the multi-model ensemble, and the ‘whiskers’ extend to the 90% uncertainty range. The dotted line indicates no change in frequency. The results are based on the multi-model ensemble from simulations of global climate models contributing to the Coupled Model Intercomparison Project Phase 6 (CMIP6) under different Shared Socio-economic Pathway forcing scenarios. Adapted from [[#Li--2021|Li et al. (2021)]] . Further details on data sources and processing are available in the chapter data table (Table 11.SM.9).

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[[File:1726f3bc034390a6043692705fb27ecd IPCC_AR6_WGI_Figure_11_7.png]]

'''Figure 11.7 |''' '''Projected changes in the frequency of extreme precipitation events under 1°C, 1.5°C, 2°C, 3°C, and 4°C global warming levels relative to the 1850''' ''–19'' ''0'' ''0 baseline.'' Extreme precipitation is defined as the annual maximum daily precipitation (Rx1day) that was exceeded on average once during a 10-year period (10-year event, blue) and once during a 50-year period (50-year event, orange) during the 1850–1900 base period. Results are shown for the global land area and the AR6 regions. For each box plot, the horizontal line and the box represent the median and central 66% uncertainty range, respectively, of the frequency changes across the multi-model ensemble, and the ‘whiskers’ extend to the 90% uncertainty range. The dotted line indicates no change in frequency. The results are based on the multi-model ensemble from simulations of global climate models contributing to the Coupled Model Intercomparison Project Phase 6 (CMIP6) under different Shared Socio-economic Pathway forcing scenarios. Adapted from [[#Li--2021|Li et al. (2021)]] . Further details on data sources and processing are available in the chapter data table (Table 11.SM.9).

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'''Table 11.1 |''' '''Synthesis table on observed changes in extremes and contribution by human influence.''' Note that observed changes in marine extremes are assessed in Cross-Chapter Box 9.1.

{| class="wikitable"
|-
! Phenomenon and Direction of Trend

! Observed/Detected Trends Since 1950 (for +0.5°C global warming or higher)

! Human Contribution to the Observed Trends Since 1950 (for +0.5°C global warming or higher)

|-
| Warmer and/or more frequent hot days and nights over most land areas

Warmer and/or fewer cold days and nights over most land areas

Warm spells/heatwaves: increases in frequency or intensity over most land areas

Cold spells/cold waves: decreases in frequency or intensity over most land areas

| ''Virtually certain'' on global scale {11.3}

'''Continental-scale evidence:'''

Asia, Australasia, Europe, North America: ''Very likely''

Central and South America: ''High confidence''

Africa: ''Medium confidence''

{11.3, 11.9}

| ''Extremely likely'' main contributor on global scale {11.3}

'''Continental-scale evidence:'''

North America, Europe, Australasia, Asia: ''Very likely''

Central and South America: ''High confidence''

Africa: ''Medium confidence''

{11.3, 11.9}

|-
| Heavy precipitation events: increase in the frequency, intensity, and/or amount of heavy precipitation

| ''Likely'' on global scale, over majority of land regions with good observational coverage {11.3}

'''Continental-scale evidence:'''

Asia, Europe, North America: ''Likely''

Africa, Australasia, Central and South America: ''Low confidence''

{11.3, 11.9}

| ''Likely'' main contributor to the observed intensification of heavy precipitation in land regions on global scale.

{11.3}

'''Continental-scale evidence:'''

Asia, Europe, North America: ''Likely''

Africa, Australasia, Central and South America: ''Low confidence''

{11.3, 11.9}

|-
| Increases in agricultural and ecological drought events

| ''Medium confidence'' some regions {11.6, 11.9}

Increasing trends in agricultural and ecological droughts have been observed in AR6 regions on all continents ( ''medium confidence'' ) {11.6, 11.9}

| ''Medium confidence'' some regions

{11.6, 11.9}

|-
| Increase in precipitation associated with tropical cyclones (TCs)

| ''Medium confidence''

{11.7}

| ''High confidence''

{11.7}

|-
| Increase in likelihood that a TC will be at major TC intensity (Cat. 3–5)

| ''Likely''

{11.7}

| ''Medium confidence''

{11.7}

|-
| Changes in frequency of rapidly intensifying tropical cyclones

| ''Likely''

{11.7}

| ''Medium confidence''

{11.7}

|-
| Poleward migration of tropical cyclones in the western Pacific

| ''Medium confidence''

{11.7}

| ''Medium confidence''

{11.7}

|-
| Decrease in TC forward motion over the USA

| It is ''likely'' that TC translation speed has slowed over the USA since 1900.

{11.7}

| It is ''more likely than not'' that the slowdown of TC translation speed over the USA has contributions from anthropogenic forcing.

{11.7}

|-
| Severe convective storms (tornadoes, hail, rainfall, wind, lightning)

| ''Low confidence'' in past trends in hail and winds and tornado activity due to short length of high-quality data records. {11.7}

| ''Low confidence''

{11.7}

|-
| Increase in compound events

| ''Likely'' increase in the probability of compound events.

''High confidence'' that concurrent heatwaves and droughts are becoming more frequent under enhanced greenhouse gas forcing at global scale.

''Medium confidence'' that fire weather, i.e. compound hot, dry and windy events, have become more frequent in some regions.

''Medium confidence'' that compound flooding risk has increased in some locations.

{11.8}

| ''Likely'' that human-induced climate change has increased the probability of compound events.

''High confidence'' that human influence has increased the frequency of concurrent heatwaves and droughts.

''Medium confidence'' that human influence has increased fire weather occurrence in some regions.

''Low confidence'' that human influence has contributed to changes in compound events leading to flooding.

{11.8}

|}

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'''Table 11.2 |''' '''Synthesis table on projected changes in extremes.''' Note that projected changes in marine extremes are assessed in [[IPCC:Wg1:Chapter:Chapter-9|Chapter 9]] and Cross-Chapter Box 9.1 (marine heatwaves). Assessments are provided compared to pre-industrial conditions.

{| class="wikitable"
|-
! Phenomenon and Direction of Trend

! Projected Changes at +1.5ºC Global Warming

! Projected Changes at +2°C Global Warming

! Projected Changes at +4°C Global Warming

|-
| Warmer and/or more frequent hot days and nights over most land areas

Warmer and/or fewer cold days and nights over most land areas

Warm spells/heatwaves; increases in frequency or intensity over most land areas

Cold spells/cold waves: decreases in frequency or intensity over most land areas

| ''Virtually certain'' on global scale

''Extremely likely'' on all continents

Highest increase of temperature of hottest days is projected in some mid-latitude and semi-arid regions, and the South American Monsoon region, at about 1.5 times to twice the rate of global warming ( ''high confidence'' ) {11.3, Figure 11.3}

Highest increase of temperature of coldest days is projected in Arctic regions, at about three times the rate of global warming ( ''high confidence'' ) {11.3}

'''Continental-scale projections:'''

''Extremely likely'' : Africa, Asia, Australasia, Central and South America, Europe, North America

{11.3, 11.9}

| ''Virtually certain'' on global scale

''Virtually certain'' on all continents

Highest increase of temperature of hottest days is projected in some mid-latitude and semi-arid regions, and the South American Monsoon region, at about 1.5 times to twice the rate of global warming ( ''high confidence'' ) {11.3, Figure 11.3}

Highest increase of temperature of coldest days is projected in Arctic regions, at about three times the rate of global warming ( ''high confidence'' ) {11.3}

'''Continental-scale projections:'''

''Virtually certain:'' Africa, Asia, Australasia, Central and South America, Europe, North America

{11.3, 11.9}

| ''Virtually certain'' on global scale

''Virtually certain'' on all continents

Highest increase of temperature of hottest days is projected in some mid-latitude and semi-arid regions, and the South American Monsoon region, at about 1.5 times to twice the rate of global warming ( ''high confidence'' ) {11.3, Figure 11.3}

Highest increase of temperature of coldest days is projected in Arctic regions, at about three times the rate of global warming ( ''high confidence'' ) {11.3}

'''Continental-scale projections:'''

''Virtually certain:'' Africa, Asia, Australasia, Central and South America, Europe, North America

{11.3, 11.9}

|-
| Heavy precipitation events: increase in the frequency, intensity, and/or amount of heavy precipitation

| ''High confidence'' that increases take place in most land regions {11.4}

''Very likely :'' Asia, North America

''Likely:'' Africa, Europe

''High confidence:'' Central and South America

''Medium confidence:'' Australasia

{11.4, 11.9}

| ''Likely'' that increases take place in most land regions {11.4}

''Extremely likely :'' Asia, North America

''Very likely :'' Africa, Europe

''Likely:'' Australasia, Central and South America

{11.4, 11.9}

| ''Very likely'' that increases take place in most land regions {11.4}

''Virtually certain:'' Africa, Asia, North America

''Extremely likely :'' Central and South America, Europe

''Very likely'' Australasia

{11.4, 11.9}

|-
| Agricultural and ecological droughts: increases in intensity and/or duration of drought events

| More regions affected by increases in agricultural and ecological droughts compared to observed changes ( ''high confidence'' ). {11.6, 11.9}

Decreased precipitation is going to increase the severity of drought in some regions; atmospheric evaporative demand will continue to increase compared to pre-industrial conditions and lead to further increases in agricultural and ecological droughts due to increased evapotranspiration in some regions. ( ''high confidence'' )

{11.6, 11.9}

| More regions affected by increases in agricultural and ecological droughts than at 1.5°C of global warming ( ''high confidence'' ). {11.6, 11.9}

Decreased precipitation is going to increase the severity of drought in some regions; atmospheric evaporative demand will continue to increase compared to pre-industrial conditions and lead to further increases in agricultural and ecological droughts due to increased evapotranspiration in some regions. ( ''high confidence'' )

{11.6, 11.9}

| More regions affected by increases in agricultural and ecological droughts than at 2°C of global warming ( ''very likely'' ) . {11.6, 11.9}

Decreased precipitation is going to increase the severity of drought in several regions; atmospheric evaporative demand will continue to increase compared to pre-industrial conditions and lead to further increases in agricultural and ecological droughts due to increased evapotranspiration in several regions. ( ''high confidence'' )

{11.6, 11.9}

|-
| Increase in precipitation associated with tropical cyclones (TCs)

| ''High confidence'' in a projected increase of TC rain rates at the global scale with a median projected increase due to human emissions of about 11%. {11.7}

''Medium confidence'' that rain rates will increase in every basin. {11.7}

| ''High confidence'' in a projected increase of TC rain rates at the global scale with a median projected increase due to human emissions of about 14%. {11.7}

''Medium confidence'' that rain rates will increase in every basin. {11.7}

| ''High confidence'' in a projected increase of TC rain rates at the global scale with a median projected increase due to human emissions of about 28%. {11.7}

''Medium confidence'' that rain rates will increase in every basin. {11.7}

|-
| Increase in mean TC lifetime-maximum wind speed (intensity)

| ''Medium confidence''

{11.7}

| ''High confidence''

{11.7}

| ''High confidence''

{11.7}

|-
| Increase in likelihood that a TC will reach major TC intensity (Category 4–5)

| ''High confidence'' for an increase in the proportion of TCs that reach the strongest (Category 4–5) levels. The median projected increase in this proportion is about 10%.

{11.7}

| ''High confidence'' for an increase in the proportion of TCs that reach the strongest (Category 4–5) levels. The median projected increase in this proportion is about 13%.

{11.7}

| ''High confidence'' for an increase in the proportion of TCs that reach the strongest (Category 4–5) levels. The median projected increase in this proportion is about 20%.

{11.7}

|-
| Severe convective storms

| colspan="3"| ''High confidence'' that the average and maximum rain rates associated with severe convective storms increase in some regions, including the USA. ''High confidence'' that convective available potential energy (CAPE) increases in response to global warming in the tropics and subtropics, suggesting more favourable environments for severe convective storms. ''Medium confidence'' that the frequency of spring severe convective storms is projected to increase in the USA, leading to a lengthening of the severe convective storm season. {11.7}

|-
| Increase in compound events (frequency, intensity)

| colspan="3"| ''Likely'' that probability of compound events will continue to increase with global warming.

''High confidence'' that concurrent heatwaves and droughts will continue to increase under higher levels of global warming, with higher frequency/intensity with every additional 0.5°C of global warming.

''High confidence'' that fire weather, (i.e. compound hot, dry and windy events), will become more frequent in some regions at higher levels of global warming.

''High confidence'' that compound flooding at the coastal zone will increase under higher levels of global warming. {11.8}

|}

<div id="box-11.2" class="h2-container box-container"></div>

Box 11.2 | Changes in Low-likelihood, High-impact Extremes

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The SREX (Chapter 3) assigned ''low confidence'' to changes in low-likelihood, high-impact (LLHI) events (termed ‘low-probability high-impact scenarios‘). Such events are often not anticipated and thus sometimes referred to as ‘surprises’. There are several types of LLHI events. Abrupt changes in mean climate are addressed in Chapter 4. Unanticipated LLHI events can either result from tipping points in the climate system ( [[IPCC:Wg1:Chapter:Chapter-1#1.4.4.3|Section 1.4.4.3]] ), such as the shutdown of the Atlantic thermohaline circulation (SROCC Chapter 6; [[#Collins--2019|Collins et al., 2019]] ) or the drydown of the Amazonian rainforest (SR1.5 Chapter 3, [[#Hoegh-Guldberg--2018|Hoegh-Guldberg et al., 2018]] ; [[#Drijfhout--2015|Drijfhout et al., 2015]] ), or from uncertainties in climate processes, including climate feedbacks, that may enhance or damp extremes either related to global or regional climate responses ( [[#Seneviratne--2018a|Seneviratne et al., 2018a]] ; [[#Sutton--2018|Sutton, 2018]] ). The ''low confidence'' does not by itself exclude the possibility of such events occuring, rather it indicates a poor state of knowledge. Such outcomes, while improbable, could be associated with very high impacts, and are thus highly relevant from a risk perspective (see [[IPCC:Wg1:Chapter:Chapter-1#1.4.3|Section 1.4.3]] and Box 11.4; [[#Sutton--2018|Sutton, 2018]] , 2019). Alternatively, high impacts can occur when different extremes occur at the same time, or in short succession at the same location, or in several regions with shared vulnerability (e.g., food-basket regions [[#Gaupp--2019|Gaupp et al., 2019]] ). These ‘compound events’ are assessed in [[#11.8|Section 11.8]] , and Box 11.4 provides a case study example.

Difficulties persist in determining the likelihood of occurrence and time frame of potential tipping points and LLHI events. However, new literature has emerged on unanticipated and LLHI events. There are some events that are sufficiently rare that they have not been observed in meteorological records, but whose occurrence is nonetheless plausible within the current state of the climate system – see examples below and in [[#McCollum--2020|McCollum et al. (2020)]] . The rare nature of such events and the limited availability of relevant data makes it difficult to estimate their occurrence probability and thus gives little evidence on whether to include such hypothetical events in planning decisions and risk assessments. The estimation of such potential surprises is often limited to events that have historical analogues (including before the instrumental records began, [[#Wetter--2014|Wetter et al., 2014]] ), albeit the magnitude of the event may differ. Additionally, there is also a limitation of available resources to exhaust all plausible trajectories of the climate system. As a result, there will still be events that cannot be anticipated. These events can be surprises to many in that the events have not been experienced, although their occurrence could be inferred by statistical means or physical modelling approaches ( [[#Chen--2017|Chen et al., 2017]] ; [[#van%20Oldenborgh--2017|van Oldenborgh et al., 2017]] ; [[#Harrington--2018a|Harrington and Otto, 2018a]] ). Another approach focusing on the estimation of low-probability events and of events whose likelihood of occurrence is unknown consists in using physical climate models to create a physically self-consistent storyline of plausible extreme events and assessing their impacts and driving factors in past ( [[#11.2.3|Section 11.2.3]] ) or future conditions ( [[#11.2.4|Section 11.2.4]] ) (Hazeleger et al. , 2015; [[#Shepherd--2016|Shepherd, 2016]] ; [[#Zappa--2017|Zappa and Shepherd, 2017]] ; Cheng et al. , 2018; Shepherd et al. , 2018; [[#Sutton--2018|Sutton, 2018]] ; Schaller et al. , 2020; Wehrli et al., 2020) .

In many parts of the world, observational data are limited to 50–60 years. This means that the chance to observe an extreme event at a particular location that occurs once in several hundred or more years is small. Thus, when a very extreme event occurs, it becomes a surprise to many ( [[#Bao--2017|Bao et al., 2017]] ; [[#McCollum--2020|McCollum et al., 2020]] ), and very rare events are often associated with high impacts (van Oldenborgh et al. , 2017; Philip et al. , 2018b; Tozer et al. , 2020). Attributing and projecting very rare events in a particular location by assessing their likelihood of occurrence within the same larger region and climate thus provides another way to make quantitative assessments regarding events that are extremely rare locally. Some examples of such events include:

* Hurricane Harvey, that made landfall in Houston, TX in August 2017 ( [[#11.7.1.4|Section 11.7.1.4]] .)
* The 2010–2011 extreme floods in Queensland, Australia ( [[#Christidis--2013a|Christidis et al., 2013a]] )
* The 2018 concurrent heatwaves across the Northern Hemisphere (Box 11.4)
* Tropical Cyclone Idai in Mozambique (Cross-Chapter Box: Disaster in WGII AR6 Chapter 4)
* The California fires in 2018 and 2019
* The 2019–2020 Australia fires (Cross-Chapter Box: Disaster in WGII AR6 Chapter 4)

One factor making such events hard to anticipate is the fact that we now live in a non-stationary climate, and that the framework of reference for adaptation is continuously moving. As an example, the concurrent heatwaves that occurred across the Northern Hemisphere in the summer of 2018 were considered very unusual and were unprecedented given the total area that was concurrently affected (Drouard et al. , 2019; Kornhuber et al. , 2019; Toreti et al. , 2019; Vogel et al. , 2019) ; however, the probability of this event under 1°C global warming was found to be about 16% ( [[#Vogel--2019|Vogel et al., 2019]] ), which is not particularly low. Similarly, the 2013 summer temperature over eastern China was the hottest on record at the time, but it had an estimated recurrence interval of about four years in the climate of 2013 ( [[#Sun--2014|Sun et al., 2014]] ). Furthermore, when other aspects of the risk, vulnerability, and exposure are historically high or have recently increased (see WGII, Chapter 16, Section 16.4), relatively moderate extremes can have very high impacts ( [[#Otto--2015b|Otto et al., 2015b]] ; [[#Philip--2018b|Philip et al., 2018b]] ). As warming continues, the climate moves further away from its historical state we are familiar with, resulting in an increased likelihood of unprecedented events and surprises. This is particularly the case under high global warming levels – for example, the climate of the late 21st century under high-emissions scenarios, above 4°C of global warming (Cross-Chapter Box 11.1).

Another factor highlighted in [[#11.8|Section 11.8]] and Box 11.4 making events high-impact and difficult to anticipate is that several locations under moderate warming levels could be affected simultaneously, or very repeatedly by different types of extremes (Mora et al., 2018; [[#Gaupp--2019|Gaupp et al., 2019]] ; [[#Vogel--2019|Vogel et al., 2019]] ). Box 11.4 shows that concurrent events at different locations, which can lead to major impacts across the world, can also result from the combination of anomalous circulation or natural variability (e.g., El Niño–Southern Oscillation) patterns with amplification of resulting responses to human-induced global warming. Also multivariate extremes at single locations pose specific challenges to anticipation ( [[#11.8|Section 11.8]] ), with low likelihoods in the current climate but the probability of occurrence of such compound events strongly increasing with increasing global warming levels ( [[#Vogel--2020a|Vogel et al., 2020a]] ). Therefore, in order to estimate whether, and at what level of global warming, very high impacts arising from extremes would occur, the spatial extent of extremes and the potential of compounding extremes need to be assessed. Sections 11.3, 11.4, 11.7 and 11.8 highlight increasing evidence that temperature extremes, higher intensity precipitation accompanying tropical cyclones, and compound events such as dry/hot conditions conducive to wildfire or storm surges resulting from sea level rise and heavy precipitation events, pose widespread threats to societies already at relatively low warming levels. Studies have already shown that the probability for some recent extreme events is so small in the undisturbed world that these events were ''extremely unlikely'' to occur without human influence ( [[#11.2.4|Section 11.2.4]] ). Box 11.2, Table 1, provides examples of projected changes in LLHI extremes (single extremes, compound events) of potential relevance for impact and adaptation assessments showing that today’s very rare events can become commonplace in a warmer future.

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'''Box 11.2, Table 1 |'''

'''Examples of changes in low-likelihood, high-impact extreme conditions (single extremes, compound events) at different global''' '''warming levels.'''
{| class="wikitable"
|-
!
! +1°C (Present-day)

! +1.5°C

! +2°C

! +3°C and Higher

|-
| Risk ratio for annual hottest daytime temperature (TXx) with 1% of probability under present-day warming (+1°C) ( [[#Kharin--2018|Kharin et al., 2018]] ): Global land

| 1

| 3.3 (i.e., 230% higher probability)

| 8.2 (i.e., 720% higher probability)

| Not assessed

|-
| Risk ratio for heavy precipitation events (Rx1day) with 1% of probability under present-day warming (+1°C) ( [[#Kharin--2018|Kharin et al., 2018]] ): Global land

| 1

| 1.2 (i.e., 20% higher probability)

| 1.5 (i.e., 50% higher probability)

| Not assessed

|-
| Number of 1–5 day duration extreme floods with 1% of probability under present-day warming (+1°C) (H. [[#Ali--2019|]] [[#Ali--2019|Ali et al., 2019]] ) Indian subcontinent

| Up to 3 in individual locations

| Up to 5 in individual locations

| 2–6 in most locations

| Up to 12 in individual locations (4°C)

|-
| Probability of ‘extreme extremes’ hot days with 1/1000 probability at the end of the 20th century ( [[#Vogel--2020a|Vogel et al., 2020a]] ): Global land

| About 20 days over 20 years in most locations

| About 50 days in 20 years in most locations

| About 150 days in 20 years in most locations

| About 500 days in 20 years in most locations (3°C)

|-
| Probability of co-occurrence in the same week of hot days with 1/1000 probability and dry days with 1/1000 probability at the end of the 20th century ( [[#Vogel--2020a|Vogel et al., 2020a]] ): Amazon

| 0% probability

| About one week in 20 years

| About 4 to 5 weeks in 20 years

| More than 9 weeks in 20 years (3°C)

|-
| Projected soil moisture drought duration per year ( [[#Samaniego--2018|Samaniego et al., 2018]] ): Mediterranean region

| 41 days (+46% compared to the late 20th century)

| 58 days (+107% compared to the late 20th century)

| 71 days (+154% compared to the late 20th century)

| 125 days (+346% compared to the late 20th century) (3°C)

|-
| Increase in days exposed to dangerous extreme heat – measured in Health Heat Index (HHI) (Q. [[#Sun--2019|]] [[#Sun--2019|]] [[#Sun--2019|]] [[#Sun--2019|Sun et al., 2019]] ) global land

| Not assessed, baseline is 1981–2000

| 1.6 times higher risk of experiencing heat &gt;40.6

| 2.3 times higher risk of experiencing heat &gt;40.6

| Around 80% of land area exposed to dangerous heat, tropical regions 1/3 of the year (4°C)

|-
| Increase in regional mean fire season length (Q. [[#Sun--2019|]] [[#Sun--2019|]] [[#Sun--2019|]] [[#Sun--2019|Sun et al., 2019]] ; [[#Xu--2020|Xu et al., 2020]] ) global land

| Not assessed, baseline is 1981–2000

| 6.2 days

| 9.5 days

| About 50 days (4°C)

|}

In summary, the future occurrence of LLHI events linked to climate extremes is generally associated with ''low confidence'' , but cannot be excluded, especially at global warming levels above 4°C. Compound events, including concurrent extremes, are a factor increasing the probability of LLHI events ( ''high confidence'' ). With increasing global warming, some compound events with low likelihood in past and current climate will become more frequent, and there is a higher chance of historically unprecedented events and surprises ( ''high confidence'' ). However, even extreme events that do not have a particularly low probability in the present climate (at more than 1°C of global warming) can be perceived as surprises because of the pace of global warming ( ''high confidence'' ).

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