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

== Atlas.5 Asia ==

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The assessment in this section focuses on changes in average temperature and precipitation (rainfall and snow), including the most recent years of observations, updates to observed datasets, the consideration of recent studies using CMIP5 and those using CMIP6 and CORDEX simulations. Assessment of changes in extremes is in [[IPCC:Wg1:Chapter:Chapter-11|Chapter 11]] (Tables 11.7–11.9) and climatic impact-drivers in [[IPCC:Wg1:Chapter:Chapter-12|Chapter 12]] (Table 12.4). It covers most Asian territories of the region (Figure Atlas.1 7) with the exception of the Russian Arctic (RAR), which is assessed as part of the Arctic in [[IPCC:Wg1:Chapter:Chapter-11#11.2|Section 11.2]] . These include West and East Siberia (WSB, ESB) and the Russian Far East (RFE) in the north; West and East Central Asia (WCA, ECA), the Tibetan Plateau (TIB) and East Asia (EAS); and the Arabian Peninsula (ARP), South and South East Asia (SAS, SEA) in the south. Figure Atlas.1 7 supports the assessment of regional mean changes in annual mean surface air temperature and precipitation over Asia. Due to the high climatological and geographical heterogeneity of Asia, the assessment is performed over five sub-continental areas: East Asia (EAS and ECA), North Asia (WSB, ESB and RFE), South Asia (SAS), South East Asia (SEA) and South West Asia (ARP and WCA) with the Tibetan Plateau (TIB) being relevant and thus referred to in both the East and South Asia assessments. Note also TIB forms a major part of the Hindu Kush Himalaya region, which is assessed in Cross-Chapter Box 10.4, and relevant findings are summarized and cross-referenced in the East and South Asia sections below.

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[[File:675ba71b0b484279198d4ee7cada124b IPCC_AR6_WGI_Atlas_Figure_17.png]]

'''Figure Atlas.17''' '''|''' '''Regional changes over land in annual mean surface air temperature and precipitation relative to the 1995–2014 baseline for the reference regions in Asia (warming since the 1850–1900 pre-industrial baseline is also provided as an offset).''' Bar plots in the left panel of each region triplet show the median (dots) and 10th–90th percentile range (bars) across each model ensemble for annual mean temperature changes for four datasets (CMIP5 in intermediate colours; a subset of CMIP5 used to drive CORDEX in light colours; CORDEX overlying the CMIP5 subset with dashed bars; and CMIP6 in solid colours); the first six groups of bars represent the regional warming over two time periods (near-term 2021–2040 and long-term 2081–2100) for three scenarios (SSP1-2.6/RCP2.6, SSP2-4.5/RCP4.5 and SSP5-8.5/RCP8.5), and the remaining bars correspond to four global warming levels (GWLs: 1.5°C, 2°C, 3°C and 4°C). The scatter diagrams of temperature against precipitation changes display the median (dots) and 10th–90th percentile ranges for the above four warming levels for December–January–February (DJF; middle panel) and June–July–August (JJA; right panel), respectively; for the CMIP5 subset only the percentile range of temperature is shown, and only for 3°C and 4°C GWLs. Changes are absolute for temperature (in °C) and relative (as %) for precipitation. See [[#Atlas.1.3|Atlas.1.3]] for more details on reference regions ( [[#Iturbide--2020|Iturbide et al., 2020]] ) and [[#Atlas.1.4|Atlas.1.4]] for details on model data selection and processing. The script used to generate this figure is available online ( [[#Iturbide--2021|Iturbide et al., 2021]] ) and similar results can be generated in the Interactive Atlas for flexibly defined seasonal periods. Further details on data sources and processing are available in the chapter data table (Table Atlas.SM.15).

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=== Atlas.5.1 East Asia ===

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==== Atlas.5.1.1 Key Features of the Regional Climate and Findings From Previous IPCC Assessments ====

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===== Atlas.5.1.1.1 Key Features of the Regional Climate =====

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The climatic regions defined for East Asia include central and eastern China, Japan and the Korea Peninsula (regions ECA and EAS in Figure Atlas.1 7). East Asia is significantly influenced by monsoon systems ( [[IPCC:Wg1:Chapter:Chapter-8#8.3.2.4.2|Section 8.3.2.4.2]] ). The seasonal advance or retreat of the East Asian summer monsoon (EASM) rainband is crucial to local climate. The East Asian winter monsoon (EAWM) has significant influence on the weather and climate over East Asia and plays an important role in regulating winter temperatures including strong cold events and snowstorms ( [[#Wang--2014|Wang and Chen, 2014]] ; [[#Wang--2016|Wang and Lu, 2016]] ). The East Asian monsoons exhibit considerable variability on a wide range of time scales, including notable interannual variabilities that includes an effect of the El Niño–Southern Oscillation (ENSO; [[#Wang--2000|Wang et al., 2000]] ) and the Indian Ocean Dipole (IOD; [[#Takaya--2020|Takaya et al., 2020]] ), and significant inter-decadal variabilities in the 20th century resulted from the effect of Pacific Decadal Variability (PDV; [[#Zhou--2009|Zhou et al., 2009]] ), see also [[IPCC:Wg1:Chapter:Annex-iv|Annex IV]] and Table Atlas.1. The thermal conditions of both the Tibetan Plateau and related ocean regions play key roles in modulating the intensity of the monsoon circulation. The East Asian monsoons are mainly driven by land–sea thermal contrast and, thus, are deeply affected by global climate change ( [[#Ding--2014|Ding et al., 2014]] ; [[#Gong--2018|Gong et al., 2018]] ).

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===== Atlas.5.1.1.2 Findings From Previous IPCC Assessments =====

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The findings of the IPCC AR5 ( [[#Christensen--2013|Christensen et al., 2013]] ) stated that the EASM and EAWM circulations have experienced an inter-decadal scale weakening since the 1970s, leading to a warmer climate in winter and enhanced mean precipitation along the Yangtze River Valley (30°N) but deficient mean precipitation in northern China in summer. Since the middle of the 20th century, it is ''likely'' that there has been an increasing trend in winter temperatures across much of Asia ( [[#Christensen--2013|Christensen et al., 2013]] ). The numbers of cold days and nights have decreased and the numbers of warm days and nights have increased over Asia ( [[#Hartmann--2013|Hartmann et al., 2013]] ). It is ''likely'' that there are decreasing numbers of snowfall events where increased winter temperatures have been observed ( [[#Hartmann--2013|Hartmann et al., 2013]] ). The SRCCL reports a land-use-change-induced cooling as large as –1.5°C in eastern China between 1871 and 2007 ( [[#Hartmann--2013|Hartmann et al., 2013]] ). The summer rainfall amount over East Asia shows no clear trend during the 20th century.

The IPCC AR5 ( [[#Christensen--2013|Christensen et al., 2013]] ) reports a significant increase in mean temperatures in south-eastern China, associated with a decrease in the number of frost days under the SRES A2 emissions scenario. The CMIP5 model projections indicate an increase of temperature in both boreal winter and summer over East Asia for RCP4.5. Based on CMIP5 model projections, there is ''medium confidence'' in an intensified EASM and increased summer precipitation over East Asia. More than 85% of CMIP5 models show an increase in mean precipitation of the EASM, while more than 95% of models project an increase in heavy precipitation events ( [[#Christensen--2013|Christensen et al., 2013]] ).The SROCC states that future projections of annual precipitation indicate increases of the order of 5–20% over the 21st century in many mountain regions, including the Himalaya and East Asia ( [[#Hock--2019b|Hock et al., 2019b]] ). The SR1.5 reports that statistically significant changes in heavy precipitation between 1.5°C and 2°C of global warming are found in East Asia ( [[#Hoegh-Guldberg--2018|Hoegh-Guldberg et al., 2018]] ).

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==== Atlas.5.1.2 Assessment and Synthesis of Observations, Trends and Attribution ====

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Summer (June–August) mean temperature in eastern China has increased by 0.82°C since reliable observations were established in the 1950s ( [[#Sun--2014|Sun et al., 2014]] ). Based on historical meteorological observations, the best estimate of the linear trend of annual mean surface air temperature (SAT) for China with 95% uncertainty ranges is 0.38°C ± 0.05°C per decade for 1979–2015 ( [[#Li--2017|Li et al., 2017]] ). From 1960 to 2010, theincreasing trend of temperature was about 0.34°C per decade in the arid region of north-west China, higher than the average over China ( [[#Li--2012|]] [[#Li--2012|B. Li et al., 2012]] ; [[#Xu--2015|Xu et al., 2015]] ). Over South Korea, warming is 1.4–2.6 times larger than global trends. The increase is 1.90°C during 1912–2014 and 0.99°C during 1973–2014 ( [[#Park--2017|Park et al., 2017]] ) with a 25–45% urbanization contribution. The annual temperature increased in large cities at a rate of 0.29°C ± 0.08°C per decade compared with 0.11°C ± 0.08°C per decade in other stations in South Korea from 1960 to 2010 (H.-S. [[#Kim--2016|]] [[#Kim--2016|Kim et al., 2016]] ). A relatively high increase in annual mean temperature at the rate of 3.0°C per century was detected in the Tokyo metropolitan area for the period 1901–2015 ( [[#Matsumoto--2017|Matsumoto et al., 2017]] ). Trends of annual temperature for the period of 1961–2015 are shown in Figure Atlas.11. Most areas of East Asia have significant warming trends exceeding 0.1°C per decade, and the strongestwarming (0.3°C–0.4°C per decade) occurs in northern China.

Observational studies indicated significant decadal variations in the EAWM ( [[#Wang--2016|Wang and Lu, 2016]] ; [[#He--2017|He et al., 2017]] ). It weakened significantly around the late 1980s, being relatively strong during 1976–1987 and weaker during 1988–2001. The EAWM has recovered in intensity after 2004 and caused frequent and prevalent severe cold spells, as well as a number of unusually harsh cold winters in many parts of East Asia during the period 2004–2012 ( [[#Wang--2014|Wang and Chen, 2014]] ; [[#Kug--2015|Kug et al., 2015]] ; [[#Ge--2016|Ge et al., 2016]] ; [[#Gong--2018|Gong et al., 2018]] ). Negative zonal mean winter SAT anomalies were observed over the whole of East Asia from 1980 to 1988, with positive anomalies observed over high and low latitudes from 1988 to 2010 ( [[#Miao--2020|Miao and Wang, 2020]] ).

Precipitation trends over East Asia show considerable regional differences ( ''medium confidence'' ). Mean precipitation has shown negligible sensitivity to the warming trend with consequently limited overall trends in China though summer rainfall daily frequency and intensity show respectively decreasing and increasing trends from 1961 to 2014 ( [[#Zhou--2017|Zhou and Wang, 2017]] ). The summer precipitation trends over eastern China display a dipole pattern, characterized by positive anomalies in central-eastern China along the Yangtze River Valley and negative anomalies in north China since the 1950s ( [[IPCC:Wg1:Chapter:Chapter-8#8.3.2.4.2|Section 8.3.2.4.2]] ). This pattern has changed with the enhanced rainfall in the Huaihe River Valley and decreased in the regions south of the middle and lower reaches of the Yangtze River Valley since the 2000s ( [[#Liu--2012|Liu et al., 2012]] ; [[#Zhao--2015|Zhao et al., 2015]] ). The climate in north-west China changed from ‘warm–dry’ to ‘warm–wet’ condition in the mid-1980s ( [[#Peng--2017|Peng and Zhou, 2017]] ; [[#Wang--2020|Wang et al., 2020]] ), with an increased rate of annual precipitation of about 3.7% per decade from 1961 to 2015 (P. [[#Wu--2019|]] [[#Wu--2019|Wu et al., 2019]] ) and 11.2 mm per decade between 1960 and 2011 in northern Xinjiang ( [[#Xu--2015|Xu et al., 2015]] ). Mean rainfall and the number of rainy days during the Meiyu-Baiu-Changma period from June to September have increased during 1973–2015 in Korea ( [[#Lee--2017|Lee et al., 2017]] ). The precipitation trend has caused a large increase in summer precipitation at a rate of 40.6 ± 4.3 mm per decade, resulting in an increase of annual precipitation of 27.7 ± 5.5 mm per decade in South Korea from 1960 to 2010 (H.-S. [[#Kim--2016|]] [[#Kim--2016|Kim et al., 2016]] ). Precipitation amounts exhibited a slight decrease at both the annual and seasonal scales in Japan for the period 1901–2012 ( [[#Duan--2015|Duan et al., 2015]] ).

Agriculture intensification through oasis expansion in Xinjiang region has increased summer precipitation in the Tian Shan mountains ( ''high confidence'' from ''medium evidence'' with ''high agreement'' ) ( [[#Zhang--2009|Zhang et al., 2009]] , 2019b; [[#Deng--2015|Deng et al., 2015]] ; [[#Guo--2015|Guo and Li, 2015]] ; [[#Yao--2016|Yao et al., 2016]] ; [[#Xu--2018|Xu et al., 2018]] ; [[#Cai--2019|Cai et al., 2019]] ). However, there is ''very low confidence'' of the effect of oasis expansion on the temperature warming trend ( [[#Han--2013|Han and Yang, 2013]] ; [[#Li--2013|Li et al., 2013]] ; [[#Yuan--2017|Yuan et al., 2017]] ).

In the context of climate warming, intense snowfalls have hit China frequently in recent winters and have caused severe damages to the sustainability of society ( [[#Sun--2019|Sun et al., 2019]] ). Observations generally show a decrease in the frequency and an increase in the mean intensity of snowfalls in north-western, north-eastern and south-eastern China and the eastern Tibetan Plateau since the 1960s ( [[#Zhou--2018|Zhou et al., 2018]] ), but the results may depend on the objective criteria for identifying winter snowfall (J. [[#Luo--2020|]] [[#Luo--2020|Luo et al., 2020]] ).

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==== Atlas.5.1.3 Assessment of Model Performance ====

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Current climate models perform poorly insimulating the mean precipitation in East Asia, including the phase of the northward progression of the seasonal rainband (M. [[#Zhang--2018|]] [[#Zhang--2018|Zhang et al., 2018]] ). Although there has been an improvement in the simulation of mean states, interannual variability and past climate changes in the progression from CMIP3 to CMIP5, some previously documented biases (such as the ridge position of the western North Pacific Subtropical High and the associated rainfall bias) are still evident in CMIP5 models ( [[#Sperber--2013|Sperber et al., 2013]] ; [[#Zhou--2017|Zhou et al., 2017]] ). Most models capture the main characteristics of the winter mean circulation over East Asia reasonably well, but they still suffer from difficulty in predicting the interannual variability of the EAWM ( [[#Shin--2018|Shin and Moon, 2018]] ). Models have improved from CMIP5 to CMIP6 for climatological temperature and EAWM (D. [[#Jiang--2020|]] [[#Jiang--2020|Jiang et al., 2020]] ). Some CMIP6 models also show improvements in simulating the annual mean and interannual variation of precipitation ( [[#Sellar--2019|Sellar et al., 2019]] ; [[#Tatebe--2019|Tatebe et al., 2019]] ; T. [[#Wu--2019|]] [[#Wu--2019|Wu et al., 2019]] ). The performance of models is sensitive to cumulus convection schemes and horizontal resolution ( [[#Haarsma--2016|Haarsma et al., 2016]] ; [[#Wu--2017|Wu et al., 2017]] ; [[#Kusunoki--2018b|Kusunoki, 2018b]] ). High-resolution atmospheric global climate models (AGCM) successfully reproduce the intensity and the spatial pattern of the EASM rainfall ( [[#Li--2015|Li et al., 2015]] ; [[#Yao--2017|Yao et al., 2017]] ; [[#Ito--2020a|Ito et al., 2020a]] ) and improve the simulation of the diurnal cycle of precipitation rates and the probability density distributions of daily precipitation over Korea, Japan and northern China ( [[#Lin--2019|Lin et al., 2019]] ), but increasing horizontal resolution (at the typical scales used in GCMs) is not always a panacea for solving model biases ( [[#Roberts--2018|Roberts et al., 2018]] ).

Recent studies using CORDEX-EA models with resolution of about 12–25 km showed that the RCMs produce relatively more detailed regional features of the temperature distribution compared with the driving GCMs ( [[#Tang--2016|Tang et al., 2016]] ). Over China, RCMs provide more spatial details and in general reduce the biases of their driving GCMs, in particular in DJF (December–January–February) and over areas with complex topography ( [[#Wu--2020|Wu and Gao, 2020]] ). However, RCMs also show biases in simulating East Asian precipitation and its variability ( [[#Park--2016|Park et al., 2016]] ; [[#Zhou--2016|Zhou et al., 2016]] ; [[#Zou--2016|Zou and Zhou, 2016]] ), and do not always show added value compared to the driving GCMs ( [[#Li--2018b|Li et al., 2018b]] ). For example, by comparing inter-GCM and inter-RCM differences around the Japan archipelago, it was found that RCM generate relatively large differences in precipitation ( [[#Suzuki-Parker--2018|Suzuki-Parker et al., 2018]] ). The RCM multi-model ensemble produces superior simulation compared to that of a single model ( [[#Jin--2016|Jin et al., 2016]] ; D.-L. [[#Guo--2018|]] [[#Guo--2018|Guo et al., 2018]] ). A comparative study of RCMs at different spatial resolutions showed that with coarse resolution they present some limitations and high-resolution RCMs offer added value for several evaluation metrics ( [[#Park--2020|Park et al., 2020]] ).

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==== Atlas.5.1.4 Assessment and Synthesis of Projections ====

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The development of climate models provides a solid basis for projection of future monsoon changes under different global warming scenarios. Coupled model simulations indicate that East Asia and the Tibetan Plateau will ''likely'' experience higher warming than the global mean conditions across all global warming levels (Figure Atlas.1 7) and with the projected warming greater in ECA and TIB than EAS. Also, in the CMIP6 ensemble, the multi-model mean and 90th percentile warming for a given period and emissions scenario are consistently greater than in the CMIP5 ensemble. Larger warming magnitudes are projected to occur in the southern, north-western, and north-eastern regions of China, parts of Mongolia, the Korean Peninsula, and Japan than in other regions ( [[#Li--2018a|Li et al., 2018a]] ). Projections indicate winter increases in SAT over the East Asian continent and in precipitation over the northern East Asian continent with 1.5°C and 2.0°C global warming under the RCP4.5 and RCP8.5 scenarios ( [[#Miao--2020|Miao et al., 2020]] ).

Projected annual precipitation changes in the CMIP5 and CMIP6 ensembles are positive for all warming levels in ECA and TIB and for the higher warming levels in EAS. Changes in precipitation per degree Celsius global warming are larger in DJF than in JJA in ECA but show smaller seasonal difference in EAS (Figure Atlas.1 7). The EASM precipitation is projected to increase but with a complex spatial structure ( [[#Kitoh--2017|Kitoh, 2017]] ; [[#Moon--2017|Moon and Ha, 2017]] ). Simulations from CMIP5 models show that compared with the current summer climate, both SAT and precipitation increase significantly over the East Asian continent during the 1.5°C warming period (L. [[#Chen--2019|]] [[#Chen--2019|Chen et al., 2019]] ) and that the main mode of EASM precipitation changes from tripolar to dipolar ( [[#Wang--2018|Wang et al., 2018]] ). The increase in precipitable water in the wet EASM region is only slightly greater than the global average but the increase in precipitation is much greater (Z. [[#Li--2019|]] [[#Li--2019|Li et al., 2019]] ). The monsoon circulation in the lower troposphere is projected to strengthen due to the enhanced thermal forcing by the Tibetan Plateau ( [[#He--2019|He et al., 2019]] ; [[#He--2020|He and Zhou, 2020]] ), which causes the increased summer precipitation over the East Asian continent. Precipitation over eastern China increases for almost all months under global warming in projections from GCMs with different horizontal resolutions ( [[#Kusunoki--2018a|Kusunoki, 2018a]] ). Also, under RCP scenarios, in the 21st century, mean precipitation is projected to increase ( [[#Kim--2020|Kim et al., 2020]] ), especially in the late afternoons ( [[#Oh--2018|Oh and Suh, 2018]] ), over the Korean Peninsula due to global warming and associated changes in EASM. Increase in JJA mean precipitation is projected in northern East Asia consistently among the CMIP models, while northward migration of early summer East Asian rainbands such as the Meiyu-Baiu-Changma is delayed along with that of the mid-latitude westerly jet in the future ( [[#Horinouchi--2019|Horinouchi et al., 2019]] ). However, the geographical distribution of precipitation change tends to depend more on the cumulus convection scheme ( [[#Ose--2017|Ose, 2017]] ) and horizontal resolution of models rather than on SST distributions. Under the RCP4.5 and the RCP8.5 scenarios, the interannual variability in EASM rainfall is projected by the multi-model ensemble mean to increase in the 21st century ( [[#Ren--2017|Ren et al., 2017]] ). Further studies show a projected increase in heavy rainfall together with increases in rainfall intensity ( [[#Endo--2017|Endo et al., 2017]] ). Multi-model intercomparison indicates significant uncertainties in future projections of climate change in East Asia, although precipitation increases consistently across models ( [[#Zhou--2017|Zhou et al., 2017]] ). Simulations under the RCP4.5 scenario project that the number of snow days will be reduced by the end of the 21st century relative to 1986–2005, primarily owing to the decline of light snowfall events. The total amount is projected to increase in north-western China but decrease in the other sub-regions ( [[#Zhou--2018|Zhou et al., 2018]] ).

The increasing temperature trends under RCP scenarios were consistently reproduced in projections using CORDEX-EA models (Y. [[#Kim--2016|]] [[#Kim--2016|Kim et al., 2016]] ) as reported in AR5 using GCMs. However, changes in annual and seasonal mean precipitation exhibit significant inter-RCM differences with larger magnitudes and variability than in the GCMs ( [[#Ham--2016|Ham et al., 2016]] ; [[#Ozturk--2017|Ozturk et al., 2017]] ; H. [[#Sun--2018|]] [[#Sun--2018|Sun et al., 2018]] ; D. [[#Zhang--2018|]] [[#Zhang--2018|Zhang et al., 2018]] ). RCM simulations project that the Meiyu-Baiu-Changma heavy rainfall will significantly increase in northern Japan at the end of the 21st century under the RCP8.5 scenario ( [[#Osakada--2018|Osakada and Nakakita, 2018]] ), but projected precipitation amount and the number of precipitation days in summer around and over Japan differ as a result of RCM uncertainty ( [[#Suzuki-Parker--2018|Suzuki-Parker et al., 2018]] ). Annual total snowfall is projected to decrease in most parts of Japan except for Japan’s northern island under RCP2.6 ( [[#Kawase--2021|Kawase et al., 2021]] ).

Projejctions based on statistical downscaling of 37 CMIP5 GCMs for Xinjiang, China, show pronounced temperature increases of 0.27°C to 0.51°C per decade from 2021 to 2060 while precipitation changes were projected to be between –1.7% to 6.8% per decade and varying seasonally and spatially ( [[#Luo--2018|Luo et al., 2018]] ). A decrease of precipitation was projected in the western region of Xinjiang during summer. More extreme rainfall events were projected to occur during summer and autumn.

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==== Atlas.5.1.5 Summary ====

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In East Asia annual mean temperature has been increasing since the 1950s ( ''high confidence'' ). The linear trend of annual mean surface air temperature ''likely'' exceeded 0.1°C per decade over most of East Asia from 1961 to 2015. Trends of annual precipitation show considerable regional differences with areas of both increases and decreases ( ''medium confidence'' ), and with increases over north-west China and South Korea ( ''high confidence'' ). Agricultural intensification through oasis expansion in Xinjiang region has increased summer precipitation in the Tian Shan mountains ( ''high confidence'' ).

GCMs still show poor performance in simulating the mean rainfall and its variability over East Asia, especially over regions characterized by complex topography. The CMIP6 models have improved from CMIP5 for climatological temperature and winter monsoon but show little improvements for the summer monsoon. The RCMs produce relatively more detailed regional features, but do not always produce superior simulations compared with the driving GCMs.

The annual mean surface temperature over East Asia and the Tibetan Plateau will ''very likely'' increase under all emissions scenarios and GWLs. Larger warming magnitudes will ''likely'' occur in the northern part of EAS and in ECA and TIB. Precipitation is ''likely'' to increase over land in most of EAS at the end of the 21st century under higher-emissions scenarios (SSP3-7.0, RCP8.5 and SSP5-8.5) and global warming levels, and in ECA and TIB under all emissions scenarios and global warming levels. Summer precipitation increase is ''likely'' to occur in East Asia, corresponding to the strengthened summer monsoon circulation.

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=== Atlas.5.2 North Asia ===

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==== [[#Atlas.5.2.1|Atlas.5.2.1]] Key Features of the Regional Climate and Findings From Previous IPCC Assessments ====

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===== Atlas.5.2.1.1 Key Features of the Regional Climate =====

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North Asia extends from the Ural Mountains in the west to the Pacific Ocean in the east and from the Russian Arctic in the north to West and East Central Asia and East Asia in the south. Its most recognizable features are boreal forests and permafrost. In AR6 North Asia is divided into three reference regions (Figure Atlas.1 7): West Siberia (WSB) with a continental climate, warm summers and cold winters, many waterlogged areas and several natural zones due to a large extent from south to north and heterogeneity in regional climates; East Siberia (ESB) which is mainly highland with extensive permafrost and a more severe continental climate characterized by harsh, long winters and short, hot summers, and by less precipitation and snow cover than in neighbouring regions; and the Russian Far East (RFE) with a monsoon-influenced climate, cold winters and wet summers in the south, and cold winters and cool summers almost without precipitation in the north. WSB and ESB are mainly influenced by NAO and NAM (Annex IV.2.1) and the Arctic Oscillation (AO) with associated atmospheric blocking by the Siberian High (SH) that exhibits a pronounced decadal-to-multi-decadal variability (see also Table Atlas.1). RFE is under the influence of the ENSO (Annex IV.2.3) and the PDV (Annex IV.2.6) that mostly affect rainfall variability.

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===== Atlas.5.2.1.2 Findings From Previous IPCC Assessments =====

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In the previous IPCC assessment cycles, the three sub-regions comprising North Asia in this section, along with Eastern Europe and the Asian Arctic, were considered as either Northern Eurasia or Russia in AR4 and AR5. The AR5 WGI stated that for North and Central Asia CMIP5 models had difficulty in representing climatological means of both temperature and precipitation, which is partly related to the scarceness of observational data in northern parts of the region and to issues related to the estimation of biases with coarse-resolution models ( [[#Christensen--2013|Christensen et al., 2013]] ). In CMIP5 projections under different RCP scenarios, North Asian temperatures increase more in winter (DJF) than summer (JJA; [[#Seneviratne--2012|Seneviratne et al., 2012]] ). With most models projecting increased precipitation significantly above the 20-year natural variability, it was concluded that precipitation in North Asia will ''very likely'' increase ( [[#Christensen--2013|Christensen et al., 2013]] ).

The SRCCL identified aridification of the climate in southern East Siberia between 1976 and 2016 as causing an extension of the steppes polewards whilst climate change also extended the vegetation season, increasing forest productivity in most of boreal Siberia, but increasing risk of wildfire and tree mortality ( [[#Mirzabaev--2019|Mirzabaev et al., 2019]] ). The SROCC noted the warming climate has caused permafrost thaw and loss of ground ice, and thus land subsidence and collapse, disturbing ecosystems and human infrastructure. Permafrost stability, hydrology and vegetation were also impacted by recent extensive fires burning into the organic soil layer ( [[#Meredith--2019|Meredith et al., 2019]] ). The SR1.5 noted that future, higher levels of warming lead to greater impacts in key systems such as the Siberian ecosystems, identified as one of the threatened systems (‘Reason for Concern 1 – RFC1’; [[#Hoegh-Guldberg--2018|Hoegh-Guldberg et al., 2018]] ) with impacts at 2°C expected to be greater than those at 1.5°C ( ''medium confidence'' ).

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<span id="atlas.5.2.2-assessment-and-synthesis-of-observations-trends-and-attribution"></span>
==== [[#Atlas.5.2.2|Atlas.5.2.2]] Assessment and Synthesis of Observations, Trends and Attribution ====

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Increases in surface air temperature (SAT) have been observed since the mid-1970s over the whole of North Asia ( [[#Frolov--2014|Frolov et al., 2014]] ), and particularly over the north-eastern part (Figure Atlas.11; [[#Gruza--2015|Gruza et al., 2015]] ). Trends of annual SAT in the northern part of the region during the last decades were ''very likely'' twice as strong as the global average (Figure Atlas.11; [[#Frolov--2014|Frolov et al., 2014]] ; [[#Mokhov--2015|Mokhov, 2015]] ; [[#Sherstyukov--2016|Sherstyukov, 2016]] ) with trends in RFE of 0.8°C–1.2°C per decade for the 1976–2014 period and more intense warming strengthening from south to north observed in spring in ESB ( [[#Frolov--2014|Frolov et al., 2014]] ; [[#Ippolitov--2014|Ippolitov et al., 2014]] ; [[#Kokorev--2015|Kokorev and Sherstiukov, 2015]] ).

Recent strong warming in polar regions (Section [[#Atlas.11.2|Atlas.11.2]] ) was accompanied by cooling in winter in mid-latitude regions particularly in the southern part of WSB and ESB ( [[#Cohen--2014|Cohen et al., 2014]] ; [[#Ippolitov--2014|Ippolitov et al., 2014]] ; [[#Gruza--2015|Gruza et al., 2015]] ; [[#Kharyutkina--2016|Kharyutkina et al., 2016]] ; [[#Overland--2016|Overland et al., 2016]] ; [[#Perevedentsev--2017|Perevedentsev et al., 2017]] ; [[#Wegmann--2018|Wegmann et al., 2018]] ). These temperature decreases were strongly correlated with significant warming over the Barents-Kara Sea (greater than 2.5°C per decade during 2003–2017) and sea ice loss, suggesting a causal link ( [[#Outten--2012|Outten and Esau, 2012]] ; [[#Semenov--2012|Semenov et al., 2012]] ; [[#Overland--2016|Overland et al., 2016]] ; [[#Semenov--2016|Semenov, 2016]] ; [[#Wegmann--2018|Wegmann et al., 2018]] ; [[#Meleshko--2019|Meleshko et al., 2019]] ; [[#Susskind--2019|Susskind et al., 2019]] ), though recent studies ( [[#Blackport--2019|Blackport et al., 2019]] ; [[#Clark--2019|Clark and Lee, 2019]] ) have shown that both phenomena result from mid-latitude circulation variability (see also Cross-Chapter Box 10.1). In addition, significant warming in the last decade has halved the cooling trend in southern WSB from –0.6°C per decade during 1976–2012 to –0.3°C per decade during 1976–2018 ( ''high confidence'' ) ( [[#Frolov--2014|Frolov et al., 2014]] ; [[#Roshydromet--2019|Roshydromet, 2019]] ).

Annual precipitation totals ''very likely'' increased over North Asia in the last half century along with more heavy and less light precipitation, more freezing rain and less freezing drizzle (Figure Atlas.11 and the Interactive Atlas; [[#Wen--2014|Wen et al., 2014]] ; [[#Groisman--2016|Groisman et al., 2016]] ; [[#Ye--2017|Ye et al., 2017]] ; [[#Chernokulsky--2019|Chernokulsky et al., 2019]] ). The highest increase was observed over regions of Siberia and RFE with estimated trends of 10–25 mm per decade for the 1976–2014 period ( [[#Kokorev--2015|Kokorev and Sherstiukov, 2015]] ) or 5% per decade for the 1976–2018 period ( [[#Roshydromet--2019|Roshydromet, 2019]] ). Increases over southern RFE are the largest (over 50 mm per decade) and are mostly due to positive changes in convective precipitation intensity in the region in the summer season (JJA) during 1966–2016 ( ''medium confidence'' ) ( [[#Chernokulsky--2019|Chernokulsky et al., 2019]] ). A decreasing trend was observed in central WSB, northern ESB, the Baikal and Transbaikal regions, the Amur River region, and Primorie territories of RFE (the Kamchatka and Chukchi peninsulas) with up to –20 mm per decade for the 1976–2014 period ( [[#Kokorev--2015|Kokorev and Sherstiukov, 2015]] ) or 15–20% per decade for the 1976–2018 period ( [[#Roshydromet--2019|Roshydromet, 2019]] ). Overall, solid precipitation predominantly decreased in North Asia and ''very likely'' caused both less snow cover extent (SCE) and snow water equivalent (SWE), attributable to the anthropogenic influence with ''high confidence'' (Sections 2.3.2.2 and 3.4.2).

Snow characteristics depend on both temperature and precipitation, and observed trends over North Asia show large spatial heterogeneity and interannual variability (Figure Atlas.1 8) leading to ''medium confidence'' that maximum snow depth has increased over Siberia, the Okhotsk Sea coast and in southern RFE since the 1960s ( [[#Callaghan--2011|Callaghan et al., 2011]] ; [[#Loginov--2014|Loginov et al., 2014]] ), with trends during 1976–2016 of 1.8 cm (in WBS), 1.1 cm (in ESB), and 4.6 cm (in RFE) per decade ( [[#Bulygina--2017|Bulygina et al., 2017]] ). Snow cover duration increased in Yakutia, Sakhalin Island and some other coastal areas of the Pacific Ocean in RFE during 1980–2009 ( [[#Callaghan--2011|Callaghan et al., 2011]] ), and decreased in WSB and ESB ( [[#Bulygina--2017|Bulygina et al., 2017]] ; [[#Roshydromet--2019|Roshydromet, 2019]] ). However, [[#Gorbatenko--2019|Gorbatenko et al. (2019)]] reported that in south-eastern WSB maximal snow depth has increased by 5–20 cm and duration of steady snow cover by between 4 and 10 days during 1989–2016 (Figure Atlas.1 8).

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[[File:73294f618704ec90a160056e6f9d1cde IPCC_AR6_WGI_Atlas_Figure_18.png]]

'''Figure Atlas.18''' '''|''' '''Linear trends for the 1980–2015 period based on station data from the World Data Centre of the Russian Institute for Hydrometeorological Information''' (RIHMI-WDC; [[#Bulygina--2014|Bulygina et al., 2014]] ). '''(a)''' Snow-season duration from 1 July to 31 December (days per decade); '''(b)''' snow-season duration from 1 January to 30 June (days per decade); '''(c)''' maximum annual height of snow cover (mm per decade). Trends have been calculated using ordinary least squares regression and the crosses indicate non-significant trend values (at the 0.1 level) following the method of [[#Santer--2008|Santer et al. (2008)]] to account for serial correlation. Further details on data sources and processing are available in the chapter data table (Table Atlas.SM.15).

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<span id="atlas.5.2.3-assessment-of-model-performance"></span>
==== [[#Atlas.5.2.3|Atlas.5.2.3]] Assessment of Model Performance ====

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Temperature trends and means derived from reanalysis datasets (JRA-25 and MERRA) correctly represented temperature variability shown in observational data over the Asian territory of Russia for the 1976–2010 period ( [[#Loginov--2014|Loginov et al., 2014]] ). Assessment of CRU TS 3.22, CRUTEMP4, ERA-Interim and NCEP2 datasets against station data over North Asia for annual and seasonal air temperature has shown that the ERA-Interim reanalysis outperforms others for the 1981–2005 period ( [[#Kokorev--2015|Kokorev and Sherstiukov, 2015]] ). The latter reanalysis also underestimates summer precipitation and shows large wet biases over north-east Asia during spring and underestimates mean seasonal temperature over north-east Asia in spring (MAM), autumn (SON), and winter (DJF), but overestimates it in summer (JJA) compared with the CRU dataset ( ''medium confidence'' ) ( [[#Ozturk--2017|Ozturk et al., 2017]] ; [[#Top--2021|Top et al., 2021]] ).

GCMs capture the main synoptic processes affecting North Asia and the CMIP5 ensemble simulates the temporal evolution of the magnitude and position of the Siberian High (SH) over the period 1872–2005 ( [[#Fei--2015|Fei and Yong-Qi, 2015]] ). CMIP5 models simulate a weakened intensity of the winter SH and a strengthened interannual variability compared to observations ( [[#Fei--2015|Fei and Yong-Qi, 2015]] ). The characteristics of blocking events over the region (number, duration, intensity and frequency) were reasonably well reproduced by GCMs ( [[#Mokhov--2014|Mokhov et al., 2014]] ), and most overestimate the annual mean temperature over northern Eurasia (Interactive Atlas). Biases in simulated annual surface air temperature simulation primarily come from the winter (DJF) season and are relatively smaller in other seasons ( [[#Miao--2014|Miao et al., 2014]] ; [[#Peng--2019|Peng et al., 2019]] ). Most GCMs capture the main decadal SAT trend ( [[#Miao--2014|Miao et al., 2014]] ), though CMIP5 GCMs fail to capture the decreasing temperature trend over East Siberia ( [[#Fei--2015|Fei and Yong-Qi, 2015]] ). Possible causes of GCMs’ inability to represent the recent slowdown of warming is further discussed in Cross-Chapter Box 3.1. For CMIP5, models with higher resolution do not always perform better than those with lower resolutions ( ''medium confidence'' ) ( [[#Miao--2014|Miao et al., 2014]] ).

Sixteen CMIP5 model simulations of SAT variability over Eurasia were evaluated against CRU observations for permafrost sub-regions ( [[#Peng--2019|Peng et al., 2019]] ), showing a warm bias in north-west Eurasia, capturing the climate warming over the 20th century and its acceleration during the late 20th century. CMIP5 GCMs generally underestimate daily temperature range compared with observations over north-eastern Russia ( [[#Sillmann--2013|Sillmann et al., 2013]] ; [[#Lindvall--2015|Lindvall and Svensson, 2015]] ). Currently there is no literature on the CMIP6 ensemble over the region though a few single-model studies are available ( [[#Voldoire--2019|Voldoire et al., 2019]] ; T. [[#Wu--2019|]] [[#Wu--2019|Wu et al., 2019]] ).

There is very limited use of RCMs for North Asia. CORDEX-CAS covers North Asia, except parts of RFE, and ARCTIC-CORDEX covers the northern regions (Figure Atlas.6). For CORDEX-CAS three RCMs (REMO, ALARO-0 and CLMcom) have been used and have warm biases for maximum temperatures, cold biases for minimum temperatures and a wet bias in the north during the winter ( [[#Top--2021|Top et al., 2021]] ). Rain gauges, however, are known to have problems in terms of measuring properly solid precipitation (e.g., due to drifting snow) which can greatly affect the accuracy of precipitation observations over North Asia ( [[#Harris--2014|Harris et al., 2014]] ).

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<span id="atlas.5.2.4-assessment-and-synthesis-of-projections"></span>
==== [[#Atlas.5.2.4|Atlas.5.2.4]] Assessment and Synthesis of Projections ====

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CMIP5 and CMIP6 projections are consistent in the direction and ranges of surface temperature change which are higher than the global average and with ensemble-mean warming of around 6°C for the 4°C GWL. Projected precipitation changes are also consistent with significant increases in winter, of up to 40% in the ensemble mean for the highest warming levels, and lower increases in summer except for WSB where changes are small and suggest drying at the 4°C GWL (Figure Atlas.1 7 and the Interactive Atlas).

The CMIP5 ensemble projects a warming of the annual mean SAT over northern Eurasia in the 21st century, ''likely'' in the range of 0.8°C–1.0°C (RCP2.6), 2.3°C–3.1°C (RCP4.5) and up to 7.2°C (RCP8.5) ( [[#Miao--2014|Miao et al., 2014]] ; [[#Peng--2019|Peng et al., 2019]] ). Mid-latitude permafrost sub-regions in Eurasia are projected to warm more than the global mean and non-permafrost territories, with ensemble area-averaged changes of 1.7°C (RCP2.6), 3.2°C (RCP4.5) or 6.4°C (RCP8.5) in 2081–2100 relative to 1986–2005 ( [[#Peng--2019|Peng et al., 2019]] ).

Over the Central Asia CORDEX domain, RegCM4.3.5 simulations driven by two different CMIP5 GCMs (HadGEM2-ES and MPI-ESM-MR) project SAT warming for 2071–2100 relative to 1971–2000 of about 3°C–4°C during the summer for RCP4.5 to over 7°C for all seasons for RCP8.5. Projected warming is most evident on the large continental Siberian Plateau with boreal and sub-boreal climates and biomes (i.e., taiga forests and tundra) during the winter season ( [[#Ozturk--2017|Ozturk et al., 2017]] ). The Voeikov Main Geophysical Observatory (MGO) RCM, driven by five CMIP5 GCMs for the RCP8.5 scenario, projects a faster increase in annual minimum temperature as compared with maximum temperature over the whole territory of Russia ( [[#Kattsov--2017|Kattsov et al., 2017]] ), and the smallest change in growing season lengths (i.e., periods with daily temperatures over 5°C, 10°C and 15°C) in the area of northern taiga in WSB and ESB comparable with other territories of Russia during the 21st century ( [[#Torzhkov--2019|Torzhkov et al., 2019]] ).

For precipitation, MGO RCM projects for the Arctic-CORDEX domain under the RCP8.5 scenario increases in annual totals for northern North Asia, a decrease in summer over ESB for 2006–2100 relative to 1951–2005 and significant increases in the upper limit of intense precipitation over most of the region in winter ( [[#Kattsov--2017|Kattsov et al., 2017]] ; [[#Khlebnikova--2018|Khlebnikova et al., 2018]] ). Other RCM projections show that in most seasons and for all future periods, precipitation in Siberia is not projected to change with respect to the 1971–2000 period, except under the RCP8.5 scenario for the winter and autumn ( [[#Ozturk--2017|Ozturk et al., 2017]] ). This very limited and controversial evidence leads to ''low confidence'' in RCM precipitation projections for North Asia and since the projections of GCMs and ESMs are more physically consistent, assessment of future precipitation changes is based on CMIP5/CMIP6 presented in Figure Atlas.1 7 and the Interactive Atlas.

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<span id="atlas.5.2.5-summary"></span>
==== [[#Atlas.5.2.5|Atlas.5.2.5]] Summary ====

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Annual surface air temperature and precipitation have ''very likely'' increased and maximum snow depth has ''likely'' increased over most of North Asia since the mid-1970s. The highest warming has been found in spring in ESB and RFE, strengthening from south to north with linear trends of 0.8°C–1.2°C per decade over the 1976–2014 period ( ''high confidence'' ). A temperature decrease was identified just in winter in the southern part of WSB and ESB as a result of natural variability, but halved from –0.6°C per decade in 1976–2012 to –0.3°C per decade for the longer 1976–2018 period due to recent warmer winters ( ''high confidence'' ). Over North Asia annual precipitation increases with estimated trends of 5–15 mm per decade in the 1976–2014 period have been recorded with an exception over the Kamchatka and the Chukchi peninsulas, where decreases of up to –20 mm per decade in the same period have been found ( ''medium confidence'' ). Snow cover duration has ''very likely'' decreased over Siberia and increases in maximum snow depths of 1.8 cm, 1.1 cm and 4.6 cm per decade have been observed for WSB, ESB and RFE respectively from 1976 to 2016 ( ''limited evidence'' ).

Most of the CMIP5 and some CMIP6 GCMs overestimate the annual mean air temperature and precipitation over the North Asia region ( ''medium confidence'' ). GCMs generally represent the observed decadal temperature trend ( ''medium confidence'' ) and biases primarily come from the winter (DJF) season ( ''high confidence'' ). Results of a very limited number of RCMs applied over the whole region show that they have warmer biases for maximum and colder biases for minimum temperatures ( ''limited evidence'' , ''medium agreement'' ). Sparsity of observational data particularly in the northern part of ESB and the whole of the RFE results in ''low confidence'' in the assessments of model performance in North Asia.

Surface air temperature and precipitation in North Asia are projected to increase further ( ''high confidence'' ) with warming higher than the global average and around 6°C at the 4°C GWL. Temperature change in 2080–2099 relative to 1981–2000 is ''likely'' in the range of 3°C in summer to 4.9°C in winter under the RCP4.5 scenario, and 5.6°C in summer to 9.7°C in winter under the RCP8.5 scenario. Precipitation is projected to increase with ensemble-mean changes of 9% in summer under both RCP4.5 and RCP8.5, and of 22% and 56% in winter respectively.

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<span id="atlas.5.3-south-asia"></span>
=== Atlas.5.3 South Asia ===

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<span id="atlas.5.3.1-key-features-of-the-regional-climate-and-findings-from-ipcc-previous-assessments"></span>
==== [[#Atlas.5.3.1|Atlas.5.3.1]] Key Features of the Regional Climate and Findings from IPCC Previous Assessments ====

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<span id="atlas.5.3.1.1-key-features-of-the-regional-climate"></span>
===== Atlas.5.3.1.1 Key Features of the Regional Climate =====

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The countries in this region are mostly semi-arid to arid and therefore depend heavily on the summer monsoon (June–September, JJAS) which is when most of the precipitation falls over the South Asia region (SAS; Figure Atlas.1 7). The topographic mechanical effect of the Tibetan Plateau (TIB) promotes moisture convergence downstream which triggers the early summer monsoon onset particularly over the Bay of Bengal and south China. In winter, westerly disturbances (WD) originating over the Atlantic Ocean bring moisture. The interaction between the WD and the Himalayas causes precipitation over northern and western parts of South Asia that is crucial to maintain the glacier mass balance. The observed teleconnection patterns over SAS for temperature show cooling effects during NAM and warming effects when in positive phase with ENSO, IOB, AMM and AMV (Annex IV). IOD also influences South Asian precipitation (Annex IV).

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<span id="atlas.5.3.1.2-findings-from-previous-ipcc-assessments"></span>
===== Atlas.5.3.1.2 Findings From Previous IPCC Assessments =====

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Recent IPCC reports assessed that it is ''very likely'' that the mean annual temperature over South Asia has increased during the past century (Figure 2.21 in [[#Hartmann--2013|Hartmann et al., 2013]] , Figure 24-2 in [[#Hijioka--2014|Hijioka et al., 2014]] ), and the frequency of cold (warm) days and nights have decreased (increased) across most of Asia since about 1950 (Figure 2.32 in [[#Hartmann--2013|Hartmann et al., 2013]] ). The AR5 assessed that there is ''high confidence'' that the large-scale patterns of surface temperature are generally well simulated by the CMIP5 models though with problems in some regions, particularly at higher elevations over the Himalayas ( [[#Flato--2013|Flato et al., 2013]] ). CMIP5 models projected for the 21st century a significant increase in temperature over South Asia ( ''high confidence'' from ''robust evidence'' ) and in projections of increased summer monsoon precipitation ( ''medium confidence'' ) ( [[#Collins--2013|Collins et al., 2013]] ). The AR5 assessed there is ''high confidence'' that high-resolution regional downscaling, which generate results complementary to those from global climate models, adds value to the simulation of spatial variations in climate in regions with highly variable topography (e.g., distinct orography, coastlines), and for mesoscale phenomena and extremes ( [[#Flato--2013|Flato et al., 2013]] ).

Inconsistent evidence was found on the declining trends in mean precipitation and increasing droughts from 1950 onwards considering 1960–1990 as the baseline period. Similarly, SREX (Table 3-3 in [[#Seneviratne--2012|Seneviratne et al., 2012]] ) reported ''low confidence'' (due to lack of literature) in trends in climate indices related to extreme precipitation events. The Indian summer monsoon circulation was found to have weakened, but this was compensated by increased local atmospheric moisture content leading to more rainfall ( ''medium confidence'' ). It is ''likely'' that the occurrence of snowfall events is decreasing in South Asia along with other regions due to an increase in winter temperatures ( [[#Hock--2019b|Hock et al., 2019b]] ). Based on satellite- and surface-based remote sensing it is ''very likely'' that aerosol optical depth has increased over southern Asia since 2000.

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<span id="atlas.5.3.2-assessment-and-synthesis-of-observations-trends-and-attribution"></span>
==== [[#Atlas.5.3.2|Atlas.5.3.2]] Assessment and Synthesis of Observations, Trends and Attribution ====

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Recent studies show that annual mean land temperatures over Indiawarmed at a rate of around 0.6°C per century during 1901–2018, which was primarily contributed by a significant increase in annual maximum temperature of 1.0°C per century, while the annual minimum temperature showed a lesser increasing trend of 0.18°C per century during this period, with a significant rise only in the recent few decades (1981–2010) at a rate of 0.17°C per decade ( [[#Srivastava--2017|Srivastava et al., 2017]] , 2019). The annual average of daily maximum and minimum temperatures has increased over almost all Pakistan with a faster increasing trend in the south ( ''high confidence'' ). Minimum temperatures have increased faster (0.17°C–0.37°C per decade) than maximum temperatures (0.17°C–0.29°C per decade) with the diurnal temperature range reduced (–0.15°C to –0.08°C per decade) in some regions ( [[#Khan--2019|Khan et al., 2019]] ).

There has been a noticeable declining trend in rainfall with monsoon deficits occurring with higher frequency in different regions in South Asia (see also [[IPCC:Wg1:Chapter:Chapter-8#8.3.2.4|Section 8.3.2.4]] on the South Asian monsoon). Concurrently, the frequency of heavy precipitation events has increased over India, while the frequency of moderate rain events has decreased since 1950 ( ''high confidence'' ) ( [[#Goswami--2006|Goswami et al., 2006]] ; [[#Dash--2009|Dash et al., 2009]] ; [[#Christensen--2013|Christensen et al., 2013]] ; [[#Krishnan--2016|Krishnan et al., 2016]] ; [[#Kulkarni--2017|Kulkarni et al., 2017]] ; [[#Roxy--2017|Roxy et al., 2017]] ). There is a considerable spread in the seasonal and annual mean precipitation climatology and interannual variability among the different observed precipitation datasets over India ( [[#Collins--2013|Collins et al., 2013]] ; [[#Prakash--2014|Prakash et al., 2014]] ; [[#Kim--2018|Kim et al., 2018]] ; [[#Ramarao--2019|Ramarao et al., 2019]] ). Yet, the regions of agreement among datasets lend ''high confidence'' that there has been a decrease in mean rainfall over most parts of the eastern and central north regions of India ( [[#Singh--2014|Singh et al., 2014]] ; [[#Roxy--2015|Roxy et al., 2015]] ; [[#Juneng--2016|Juneng et al., 2016]] ; [[#Krishnan--2016|Krishnan et al., 2016]] ; [[#Guhathakurta--2017|Guhathakurta and Revadekar, 2017]] ; [[#Jin--2017|Jin and Wang, 2017]] ; [[#Latif--2017|Latif et al., 2017]] ). A global modelling study with high resolution over South Asia ( [[#Sabin--2013|Sabin et al., 2013]] ) indicated that a juxtaposition of regional land-use changes, anthropogenic-aerosol forcing and the rapid warming signal of the Equatorial Indian Ocean was crucial to simulate the observed Indian summer monsoon weakening in recent decades ( ''medium confidence'' ).

A dipole-like structure in summer monsoon rainfall trends is observed over the northern Indo-Pakistan area with significant increases over Pakistan and decreases over central north India resulting from strengthening (weakening) of vertically integrated meridional moisture transport over the Arabian Sea (Bay of Bengal) ( ''low confidence'' ) ( [[#Latif--2017|Latif et al., 2017]] ). Positive annual precipitation trends are observed in global and regional datasets (Figure Atlas.11 and the Interactive Atlas) during 1961–2015 and over arid provinces of Pakistan (for rabi and kharif cropping seasons) during 1951–2015 of 2.8–34.8 mm per decade ( [[#Khan--2020|Khan et al., 2020]] ) imply ''high confidence'' for increased precipitation in Pakistan. Observations located in the monsoon-dominated strip in Pakistan indicate that the mean monsoon onset became earlier during 1971–2010 ( [[#Ali--2020|Ali et al., 2020]] ).

Snow and glaciers are major water resources of all countries in South Asia. Glacier melting is mainly controlled by natural phenomena but anthropogenic emissions of black carbon (BC) are now making a significant contributing to total glacial melting in the Hindu Kush Himalaya (HKH) region ( [[#Menon--2002|Menon, 2002]] ; [[#Ramanathan--2007|Ramanathan et al., 2007]] ; [[#Ramanathan--2008|Ramanathan and Carmichael, 2008]] ). BC concentration is seven to 10 times higher in mid-altitudes (1000–4000 metres above sea level) than at high altitudes (&gt;4000 metres above sea level). The concentration of BC sampled from the surface of snow/ice samples as well as ice-core records shows decreasing ice albedo and an acceleration in glacier melting (Cross-Chapter Box 10.4; [[#Wester--2019|Wester et al., 2019]] ). Karakoram and western HKH snow cover is increasing, a phenomena known as the ‘Karakoram anomaly’, and partially attributed to an increase in the strength of westerly disturbances ( [[#Wester--2019|Wester et al., 2019]] ).

Significant glacier retreat has been observed since 1960 in TIB with lower rates in the interior of the region ( [[#Yao--2007|Yao et al., 2007]] ). A large inter-decadal variation in snow cover is also observed from 1960 to 2010. Observations and model simulations showed that the increasing temperature of frozen grounds is leading to thawing and reduced depth of permafrost, with further significant reductions projected under future global warming scenarios ( ''medium confidence'' ) ( [[#Yang--2019|Yang et al., 2019]] ).

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<span id="atlas.5.3.3-assessment-of-model-performance"></span>
==== [[#Atlas.5.3.3|Atlas.5.3.3]] Assessment of Model Performance ====

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Whilst simulations of Indian summer monsoon rainfall (ISMR) have improved in CMIP5 compared to CMIP3 in terms of northward propagation, time for peak monsoon and withdrawal ( [[#Sperber--2013|Sperber et al., 2013]] ), they fail to simulate the trends in monsoon rainfall and the post-1950 weakening of monsoon circulation ( [[#Saha--2014|Saha et al., 2014]] ). This is partially attributed to the failure of coarse-resolution CMIP5 models to simulate fine-resolution processes such as orographic effects or land surface feedback, and problems in cloud parametrization result in an overestimation of convective precipitation fraction (M.S. [[#Singh--2017|]] [[#Singh--2017|Singh et al., 2017]] ). In CMIP6, a significant improvement is found in capturing the monsoon spatio-temporal patterns over India, particularly in the Western Ghats and north-eastern Himalayan foothills ( [[#Gusain--2020|Gusain et al., 2020]] ). Over Pakistan the CMIP6 models simulate surface temperature better in JJA than DJF ( [[#Karim--2020|Karim et al., 2020]] ). The CMIP6 ensemble underestimates annual mean temperature over all of South Asian with mixed results for precipitation ( [[#Almazroui--2020b|Almazroui et al., 2020b]] ). The CMIP6 GCMs have a large cold bias in both mean annual maximum and minimum temperatures in the complex Karakorum and Himalayan mountain ranges but exhibit warm biases in mean annual minimum temperature in most of the rest of South Asia.

Regional climate model (RCM) downscaling of CMIP5 models as part of CORDEX South Asia uses higher resolution (50 km) and improved surface fields such as topography and coastlines to resolve better the complexities of the monsoon and other hydrological processes ( [[#Giorgi--2009|Giorgi et al., 2009]] ). The added value of their simulations, relative to the driving GCMs, presents a complex picture. CORDEX RCMs better represent spatial patterns of temperature ( [[#Sanjay--2017|Sanjay et al., 2017]] ), the spatial features of precipitation distribution associated with the Indian summer monsoon ( [[#Choudhary--2018|Choudhary and Dimri, 2018]] ), and the simulation of monsoon active- and break-phase composite precipitation ( [[#Karmacharya--2017b|Karmacharya et al., 2017b]] ). The RCMs follow the driving GCMs in underestimating seasonal mean surface air temperature and overestimating spatial variability in precipitation. They amplify CMIP5 cold biases over almost the entire region, including over the HKH region, Afghanistan and south-west Pakistan during winter ( [[#Iqbal--2017|Iqbal et al., 2017]] ), and substantial cold biases of 6°C–10°C are found over the Himalayan watersheds of the Indus basin ( [[#Nengker--2018|Nengker et al., 2018]] ; [[#Hasson--2019|Hasson et al., 2019]] ). Neither RCMs nor their driving CMIP5 GCMs reproduce well the region’s precipitation climatology ( [[#Mishra--2015|Mishra, 2015]] ). In addition, important characteristics of ISMR such as northward and eastward propagation, onset, seasonal rainfall patterns, intra-seasonal oscillations and patterns of extremes did not show consistent improvement (S. [[#Singh--2017|]] [[#Singh--2017|Singh et al., 2017]] ). Also, these RCM simulations have not demonstrated added value in capturing the observed changes in ISMR characteristics over recent decades, though RegCM4 simulations at 25 km showed high accuracy in capturing monsoon precipitation characteristics and atmospheric dynamics in historical simulations ( [[#Ashfaq--2021|Ashfaq et al., 2021]] ).

Evaluation of four global reanalysis products (ERA5 and ERA-Interim, JRA-55 and MERRA-2; [[#Atlas.1.4.2|Atlas.1.4.2]] ) for snow depth and snow cover over TIB was performed against 33 in situ station observations, Interactive Multisensor Snow and Ice Mapping System (IMS) snow cover and a satellite microwave snow-depth dataset ( [[#Orsolini--2019|Orsolini et al., 2019]] ). Most of the reanalyses showed a systematic overestimation. Only ERA-Interim assimilated IMS snow cover at high altitudes, whereas ERA5 did not and the excessive snowfall, snow depth and snow cover in ERA5 was attributed to this difference. The analysis of annual maximum consecutive snow-covered days for the period 1980–2018 over TIB using JRA-55 and passive microwave satellite observations showed a decreasing trend in all time periods and in recent snow seasons for MERRA-2 ( [[#Bian--2020|Bian et al., 2020]] ). The uncertainty assessment of model physics in snow modelling over TIB using ground-based observations and high-resolution snow cover satellite products from the Moderate Resolution Imaging Spectroradiometer (MODIS) and FengYun-3B suggests that errors can be overcome by optimizing parametrizations of the snow cover fraction rather than optimizing physics-scheme options (Y. [[#Jiang--2020|]] [[#Jiang--2020|Jiang et al., 2020]] ).

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<span id="atlas.5.3.4-assessment-and-synthesis-of-projections"></span>
==== [[#Atlas.5.3.4|Atlas.5.3.4]] Assessment and Synthesis of Projections ====

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CMIP5 and CMIP6 surface temperature projections for South Asia are consistent across the range of GWLs withincreases greater than the global average, more so over TIB (Figure Atlas.1 7). CMIP6 models show higher sensitivity to greenhouse gas emissions, projecting higher warming for a given emissions scenario. The north-western parts of South Asia, mainly covering the Karakorum and Himalayan mountain ranges, are projected to warm more (over 6°C under SSP5-8.5, with higher warming in winters than in summer; Interactive Atlas) and this will accelerate glacier melting in the region. The warming pattern of maximum and minimum temperatures are projected to intensify in higher latitudes compared with mid-latitudes of South Asia in CMIP5 simulations for all RCP scenarios ( [[#Ullah--2020|Ullah et al., 2020]] ).

Seasonal precipitation projections show increased winter precipitation over the western Himalayas and decreased precipitation over the eastern Himalayas. On the other hand, summer precipitation projections show a robust increase over most of South Asia, with the largest over the arid region of southern Pakistan and in adjacent areas of India, under SSP5-8.5 ( [[#Almazroui--2020b|Almazroui et al., 2020b]] ). Daily bias-adjusted projections from 13 CMIP6 GCMs using all emissions scenarios project a warmer (3°C–5°C) and wetter (13–30%) climate in South Asia in the 21st century ( [[#Mishra--2020|Mishra et al., 2020]] ).

With continued global warming and anticipated reductions in anthropogenic aerosol emissions in the future, CMIP5 models project an increase in the mean and variability of summer monsoon precipitation over India by the end of the 21st century, together with substantial increases in daily precipitation extremes ( ''medium confidence'' ) ( [[#Krishnan--2020|Krishnan et al., 2020]] ), see also [[IPCC:Wg1:Chapter:Chapter-8#8.4.2.4|Section 8.4.2.4]] on changes in the South Asian monsoon. The CMIP5 GCMs consistently project an increase in moisture transport over the Arabian Sea and Bay of Bengal towards the end of the 21st century, an increase in moisture convergence and consequent increases in monsoon rainfall over the Indo-Pakistan region which are higher under RCP8.5 than RCP4.5 ( [[#Srivastava--2014|Srivastava and Delsole, 2014]] ; [[#Mei--2015|Mei et al., 2015]] ; [[#Latif--2018|Latif et al., 2018]] ). Out of 20 CMIP5 GCMs, four showed an increase in magnitude and lengthening of the summer monsoon across India under RCP8.5. The intensity of both strong and weak monsoons is projected to increase during the period 2051–2099 ( [[#Srivastava--2014|Srivastava and Delsole, 2014]] ).

Summer precipitation changes in South Asia are consistent between CMIP3 and CMIP5 projections, but the model spread is large for winter precipitation changes. Changes in summer monsoon rainfall will dominate annual changes over South Asia ( [[#Woo--2019|Woo et al., 2019]] ). CMIP3 GCMs project a gradual increase in annual precipitation over monsoon-dominated areas of Pakistan throughout the 21st Century and increases in humid and semi-arid climate areas ( [[#Saeed--2018|Saeed and Athar, 2018]] ).

Warming of 2.5°C–5°C is projected over northern Pakistan and India ( [[#Syed--2014|Syed et al., 2014]] ). CORDEX-South Asia projections over north-east India under RCP4.5 for the period 2011–2060, show increasing trends for both seasonal maximum and minimum temperature over north-east India (Interactive Atlas). The future projections of South Asian monsoon from the CORDEX-CORE exhibit a spatially robust delay in the monsoon onset, an increase in seasonality, and a reduction in the rainy season length over parts of South Asia at higher levels of radiative forcing ( [[#Ashfaq--2021|Ashfaq et al., 2021]] ).

With TIB continuing to warm, snow cover and snow water equivalent are projected to decrease but with regional differences due to synoptic influences (Cross-Chapter Box 10.4; [[#Wester--2019|Wester et al., 2019]] ). There is ''limited evidence'' on whether the ‘Karakoram Anomaly’ will persist in coming decades, but its long-term persistence is ''unlikely'' with continued projected warming ( ''high confidence'' ) ( [[IPCC:Wg1:Chapter:Chapter-9#9.5.1.1|Section 9.5.1.1]] ). It is projected that peak river flow at higher altitudes will commence earlier, due to warming influences on snow cover area and snow/glacier melt rates and with more precipitation falling as rain rather than snow, and the magnitude and seasonality of flow will change over South Asia ( [[#Charles--2016|Charles et al., 2016]] ).

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<span id="atlas.5.3.5-summary"></span>
==== [[#Atlas.5.3.5|Atlas.5.3.5]] Summary ====

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Mean, minimum and maximum daily temperatures in South Asia are increasing and winters are getting warmer faster than summers ( ''high confidence'' ). The South Asian monsoon has shown contrasting behaviour over India and Pakistan. There is ''high confidence'' that there has been a decrease in mean rainfall over most parts of the eastern and central north regions of India and an increase in precipitation in Pakistan.

Global model performance over the region has improved from CMIP3 to CMIP5 to CMIP6 in the multi-model ensemble-mean simulation of the amplitude and phase of the seasonal cycles of temperature and precipitation. However, there was no appreciable improvement in regions with steep orography, and there has remained substantial inter-model spread in seasonal and annual mean temperatures over South Asia with generally cold biases which are largest in the complex Karakorum and Himalayan mountain ranges. CMIP6 GCMs also show a dry bias (15–20%) in mean annual precipitation in the majority of the South Asia region with a wet bias in Nepal, Pakistan and northern India.

It is ''likely'' that surface temperatures over South Asia will increase more than the global average and more so over TIB, with projected increases of 4.6°C (3.4°C–6.0°C) during 2081–2100 compared with 1995–2014 under SSP5-8.5 and 1.3°C (0.7°C– 2.0°C) under SSP1-2.6 (Interactive Atlas). Summer monsoon precipitation in South Asia is ''likely'' to increase by the end of the 21st century while winter monsoons are projected to be drier. Over the same time periods CMIP6 models project an increase in annual precipitation in the range 14–36% under SSP5-8.5 and 0.4–16% under SSP1-2.6 ( ''medium confidence'' ).

With continued warming, TIB snow cover and snow water equivalent are ''likely'' to decrease and with more precipitation falling as rain rather than snow in SAS. It is projected that the peak river flow at higher altitudes will commence earlier due to the effect of warming on snow cover and snow/glacier melt rates, causing changes in magnitude and seasonality of flow.

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<span id="atlas.5.4-south-east-asia"></span>
=== Atlas.5.4 South East Asia ===

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<span id="atlas.5.4.1-key-features-of-the-regional-climate-and-findings-from-previous-ipcc-assessments"></span>
==== [[#Atlas.5.4.1|Atlas.5.4.1]] Key Features of the Regional Climate and Findings from Previous IPCC Assessments ====

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<span id="atlas.5.4.1.1-key-features-of-the-regional-climate"></span>
===== Atlas.5.4.1.1 Key Features of the Regional Climate =====

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The South East Asia region is composed of countries that are part of Indochina (or mainland South East Asia) and countries that are very archipelagic in nature and have strong land-ocean-atmosphere interactions, including those that are part of the Maritime Continent and the Philippines. Its climate is mainly tropical (i.e., hot and humid with abundant rainfall). Rainfall seasonal variability in the region is mainly affected by the synoptic-scale monsoon systems, the north–south migration of the Inter-tropical Convergence Zone (ITCZ) and tropical cyclones (mainly for the Philippines and Indochina), while intra-seasonal variability can be influenced by the MJO (Annex IV). Temperature and especially rainfall are also interannually affected by ENSO and Indian Ocean basin and Dipole (IOB/IOD) modes ( [[IPCC:Wg1:Chapter:Annex-iv|Annex IV]] and Table Atlas.1).

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<span id="atlas.5.4.1.2-findings-from-previous-ipcc-assessments"></span>
===== Atlas.5.4.1.2 Findings From Previous IPCC Assessments =====

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The AR5 WGI showed that the mean annual temperature of South East Asia has been increasing at a rate of 0.14°C–0.20°C per decade since the 1960s, along with an increasing number of warm days and nights, and a decreasing number of cold days and nights ( [[#Christensen--2013|Christensen et al., 2013]] ). The AR5 also reported the lack of sufficient observational records to allow for a full understanding of past precipitation trends in most of the Asian region, including South East Asia, and that precipitation trends that were available differed considerably across the region and between seasons ( [[#Christensen--2013|Christensen et al., 2013]] ).

On projected changes, findings from AR5 showed that warming is ''very likely'' to continue with substantial sub-regional variations over South East Asia ( [[#Christensen--2013|Christensen et al., 2013]] ). The median increase in temperature over land projected by the CMIP5 ensemble mean ranges from 0.8°C in RCP2.6 to 3.2°C in RCP8.5 by the end of the 21st century. Moderate future increases in precipitation are ''very likely'' , with projected ensemble mean increases of 1% in RCP2.6 to 8% in RCP8.5 by 2100. In SR1.5, there is a projected increase in flooding and runoff over South East Asia for a 1.5°C to 2°C global warming, and these will increase even more for a greater than 2°C level of warming ( [[#Hoegh-Guldberg--2018|Hoegh-Guldberg et al., 2018]] ).

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<span id="atlas.5.4.2-assessment-and-synthesis-of-observations-trends-and-attribution"></span>
==== [[#Atlas.5.4.2|Atlas.5.4.2]] Assessment and Synthesis of Observations, Trends and Attribution ====

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Within the last decade, there has been an increasing number of studies on climatic trends over South East Asia, carried out on a regional basis ( [[#Thirumalai--2017|Thirumalai et al., 2017]] ; [[#Cheong--2018|Cheong et al., 2018]] ) or focused on specific countries ( [[#Cinco--2014|Cinco et al., 2014]] ; [[#Villafuerte--2014|Villafuerte et al., 2014]] ; [[#Mayowa--2015|Mayowa et al., 2015]] ; [[#Villafuerte--2015|Villafuerte and Matsumoto, 2015]] ; [[#Guo--2017a|Guo et al., 2017a]] ; [[#Supari--2017|Supari et al., 2017]] ; [[#Sa’adi--2019|Sa’adi et al., 2019]] ; [[#Tan--2021|Tan et al., 2021]] ). They document ''virtually certain'' significant increases in mean as well as extreme temperature. The minimum temperature extremes ''very likely'' warmed faster compared to the maximum temperature. Temperatures, including extremes, are strongly influenced by ENSO in the region ( [[#Cinco--2014|Cinco et al., 2014]] ; [[#Thirumalai--2017|Thirumalai et al., 2017]] ; [[#Cheong--2018|Cheong et al., 2018]] ). Over much of the region, extreme high temperatures occurred mostly in April and almost all April extreme temperatures occur in El Niño years ( [[#Thirumalai--2017|Thirumalai et al., 2017]] ). In most of South East Asia (except for the north-eastern areas), there was ''likely'' an increase in the number of warm nights with El Niño episodes within the period 1972–2010 ( [[#Cheong--2018|Cheong et al., 2018]] ).

Changes in mean precipitation are less spatially coherent over South East Asia. Over Thailand, the average number of rain days has decreased by 1.3 to 5.9 days per decade while average daily rainfall intensity has increased by 0.24–0.73 mm day <sup>–1</sup> per decade ( [[#Limsakul--2016|Limsakul and Singhruck, 2016]] ). Precipitation is also affected by ENSO events ( [[#Tangang--2017|Tangang et al., 2017]] ; Supari et al., 2018). Over South East Asia, there has been a significant increase in the amount of precipitation and its extremes with La Niña episodes in the past decades, especially during the winter monsoon period ( ''high confidence'' ) ( [[#Villafuerte--2015|Villafuerte and Matsumoto, 2015]] ; [[#Limsakul--2016|Limsakul and Singhruck, 2016]] ; [[#Cheong--2018|Cheong et al., 2018]] ).

Figure Atlas.11 shows trends in mean temperature and precipitation during 1961–2015 for two global datasets, indicating a significant overall warming over South East Asia ( ''high confidence'' ), with higher rates of warming in Malaysia, Indonesia, and the southern areas of mainland South East Asia ( ''low confidence'' ). Annual mean precipitation trends ( [[#Atlas.1.4.1|Atlas.1.4.1]] and the Interactive Atlas, which includes the regional dataset Aphrodite) over the region are mostly not significant except for increases over parts of Malaysia, Vietnam and the southern Philippines ( ''medium confidence'' ).

It is important to note that the availability, quality, and temporal and spatial density of observation data may lead to uncertainties and varying results in South East Asia ( [[#Juneng--2016|Juneng et al., 2016]] ). Some efforts have been made to produce better observationally-based gridded datasets for the region (e.g., [[#Nguyen-Xuan--2016|Nguyen-Xuan et al., 2016]] ; [[#van%20den%20Besselaar--2017|van den Besselaar et al., 2017]] ; [[#Yatagai--2020|Yatagai et al., 2020]] ).

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<span id="atlas.5.4.3-assessment-of-model-performance"></span>
==== [[#Atlas.5.4.3|Atlas.5.4.3]] Assessment of Model Performance ====

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Performance in simulating rainfall over South East Asia varies among CMIP5 GCMs ( ''high confidence'' ). Only some are capable of reasonably simulating the rainfall seasonal cycle and spatial pattern ( [[#Siew--2013|Siew et al., 2013]] ; [[#Raghavan--2018|Raghavan et al., 2018]] ). Over mainland South East Asia, the performance of CMIP5 GCMs in simulating rainfall during the wet season was superior to that for annual and dry-season precipitation (J. [[#Li--2019|]] [[#Li--2019|Li et al., 2019]] ).

RCMs have been intensively used over the region in recent years in a series of single or multi-model experiments and there is ''medium confidence'' that they reproduce reasonably well seasonal climate patterns of temperature, precipitation and large-scale circulation over the different sub-regions of South East Asia with added values compared to their host GCMs ( [[#Kwan--2014|Kwan et al., 2014]] ; [[#Ngo-Duc--2014|Ngo-Duc et al., 2014]] , 2017; [[#Van%20Khiem--2014|Van Khiem et al., 2014]] ; [[#Juneng--2016|Juneng et al., 2016]] ; [[#Katzfey--2016|Katzfey et al., 2016]] ; [[#Loh--2016|Loh et al., 2016]] ; [[#Raghavan--2016|Raghavan et al., 2016]] ; [[#Cruz--2017|Cruz et al., 2017]] ; [[#Ratna--2017|Ratna et al., 2017]] ; [[#Trinh-Tuan--2018|Trinh-Tuan et al., 2018]] ; [[#Nguyen-Thuy--2021|Nguyen-Thuy et al., 2021]] ). RCM ensemble means tend to outperform the individual models in representing the climatological mean state ( [[#Ngo-Duc--2014|Ngo-Duc et al., 2014]] ; [[#Trinh-Tuan--2018|Trinh-Tuan et al., 2018]] ; [[#Nguyen-Thi--2021|Nguyen-Thi et al., 2021]] ). There is relatively high consistency among the simulations of historical climate over mainland South East Asia compared to those over the Maritime Continent for both seasonal and interannual variability ( [[#Ngo-Duc--2017|Ngo-Duc et al., 2017]] ). The consistency in rainfall simulations was lower than for temperature simulations.

Some RCMs showed a systematic cold bias ( [[#Manomaiphiboon--2013|Manomaiphiboon et al., 2013]] ; [[#Kwan--2014|Kwan et al., 2014]] ; [[#Ngo-Duc--2014|Ngo-Duc et al., 2014]] ; [[#Loh--2016|Loh et al., 2016]] ; [[#Cruz--2017|Cruz and Sasaki, 2017]] ; [[#Cruz--2017|Cruz et al., 2017]] ) that was mainly due to model physics ( [[#Manomaiphiboon--2013|Manomaiphiboon et al., 2013]] ; [[#Kwan--2014|Kwan et al., 2014]] ) and/or the biases in the SST forcing ( [[#Ngo-Duc--2014|Ngo-Duc et al., 2014]] ). A few simulations revealed a warm bias over some areas such as in the Maritime Continent ( [[#Cruz--2017|Cruz et al., 2017]] ) or Vietnam ( [[#Van%20Khiem--2014|Van Khiem et al., 2014]] ). The biases for rainfall in GCMs and RCMs over South East Asia were found to be less systematic with wet or dry biases depending on the sub-regions ( [[#Manomaiphiboon--2013|Manomaiphiboon et al., 2013]] ; [[#Kwan--2014|Kwan et al., 2014]] ; [[#Van%20Khiem--2014|Van Khiem et al., 2014]] ; [[#Juneng--2016|Juneng et al., 2016]] ; Supari et al., 2020; [[#Tangang--2020|Tangang et al., 2020]] ; [[#Nguyen-Thi--2021|Nguyen-Thi et al., 2021]] ), although wet biases were more pronounced in RCMs ( [[#Kwan--2014|Kwan et al., 2014]] ; [[#Van%20Khiem--2014|Van Khiem et al., 2014]] ; [[#Kirono--2015|Kirono et al., 2015]] ; [[#Juneng--2016|Juneng et al., 2016]] ; Supari et al., 2020; [[#Tangang--2020|Tangang et al., 2020]] ). Some RCMs overestimated rainfall interannual variability ( [[#Juneng--2016|Juneng et al., 2016]] ) while some others underestimated it ( [[#Kirono--2015|Kirono et al., 2015]] ). Simulated rainfall amount is sensitive to the choice of convective scheme ( [[#Juneng--2016|Juneng et al., 2016]] ; [[#Ngo-Duc--2017|Ngo-Duc et al., 2017]] ) and the choice of land surface scheme ( [[#Chung--2018|Chung et al., 2018]] ). Rainfall biases in current climate simulations can be greatly reduced if a bias adjustment method such as quantile mapping is applied ( [[#Trinh-Tuan--2018|Trinh-Tuan et al., 2018]] ). The pattern of tropical cyclone numbers in the region were reasonable represented by RCM outputs ( [[#Van%20Khiem--2014|Van Khiem et al., 2014]] ; [[#Kieu-Thi--2016|Kieu-Thi et al., 2016]] ; [[#Herrmann--2020|Herrmann et al., 2020]] ).

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<span id="atlas.5.4.4-assessment-and-synthesis-of-projections"></span>
==== [[#Atlas.5.4.4|Atlas.5.4.4]] Assessment and Synthesis of Projections ====

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Mean temperature in South East Asia is projected to continue to rise through the 21st century ( ''virtually certain'' , ''very high confidence'' ). Projections by multi-model regional climate simulations of CORDEX-SEA showed a temperature increment over land under RCP8.5 to range from 3°C–5°C by the end of the 21st century relative to the pre-1986–2005 period ( [[#Tangang--2018|Tangang et al., 2018]] ). For the same periods, the average mean temperature increase over land projected by CMIP5 (CMIP6) varies, with 10th–90th percentile ranges, from 0.7°C to 1.3°C (0.7°C to 1.8°C) under RCP2.6 (SSP1-2.6) to 2.8°C to 4.4°C (2.6°C to 4.8°C) under RCP8.5 (SSP5-8.5) (Interactive Atlas). For all GWLs the land region is projected to warm by a slightly smaller amount than the global average, with 10th–90th percentile ranges for CMIP5 (CMIP6) of 1.2°C–1.6°C (1.2°C–1.5°C) for the 1.5°C GWL and of 3.3°C–4.0°C (3.3°C–3.9°C) for the 4°C GWL relative to the 1850–1900 baseline (calculated from RCP8.5 (SSP5-8.5) projections). Changes for other warming levels, periods and emissions pathways are shown in Figure Atlas.1 7 and can be explored in the Interactive Atlas.

Projections of future rainfall changes are highly variable among sub-regions of South East Asia and among the models ( ''high confidence'' ). The CMIP5 and CMIP6 ensembles showed an increase in annual mean precipitation over most land areas by the mid- and late 21st century, although only with a strong model agreement for higher warming levels (Figure Atlas.1 7 and the Interactive Atlas), while CORDEX produces a general decrease in projected precipitation (Figure Atlas.1 7). Based on CORDEX South East Asia multi-model simulations, significant and robust increases of mean rainfall over Indochina and the Philippines were projected while there is a drying tendency over the Maritime Continent during DJF for the early, mid and end of the 21st century periods under both RCP4.5 and RCP8.5 (Figure Atlas.1 9; [[#Tangang--2020|Tangang et al., 2020]] ). At the end of the 21st century during DJF and under RCP8.5, an increase of 20% in mean rainfall is projected over Myanmar, northern central Thailand and northern Laos, and of 5–10% over the eastern Philippines and northern Vietnam. During JJA, significantly drier conditions are projected over almost the entire South East Asia region except over Myanmar and northern Borneo. Over the Indonesian region, especially Java, Sumatra and Kalimantan, as much as a 20–30% decrease in mean rainfall is projected during JJA by the end of the 21st century. The projected drier condition over Indonesia from CORDEX is consistent with that of [[#Kusunoki--2017|Kusunoki (2017)]] , [[#Giorgi--2019|Giorgi et al. (2019)]] , [[#Kang--2019|Kang et al. (2019)]] and Supari et al. (2020) and is associated with enhanced subsidence over the region ( [[#Kang--2019|Kang et al., 2019]] ; [[#Tangang--2020|Tangang et al., 2020]] ).

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[[File:d8e07088a9b5c23845ff3b862e06780d IPCC_AR6_WGI_Atlas_Figure_19.png]]

'''Figure Atlas.19''' '''|''' '''The RCM-projected changes in mean precipitation between the early (2011–2040), mid- (2041–2070) and late (2071–2099) 21st century and the historical period 1976–2005.''' Data are obtained from the CORDEX-SEA downscaling simulations. Diagonal lines indicate areas with low model agreement (less than 80%). Figure adapted from [[#Tangang--2020|Tangang et al. (2020)]] .

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<span id="atlas.5.4.5-summary"></span>
==== [[#Atlas.5.4.5|Atlas.5.4.5]] Summary ====

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It is ''virtually certain'' that annual mean temperature has been increasing in South East Asia in the past decades while changes in annual mean precipitation are less spatially coherent though with some increasing trends over parts of Malaysia, Vietnam and the southern Philippines ( ''medium confidence'' ).

Although various biases still exist, there is ''high confidence'' that the models can reproduce seasonal climate patterns well over the different sub-regions of South East Asia. There is ''medium confidence'' that the RCMs show added value compared to their host GCMs over the region.

Projections show continued warming over South East Asia, but ''likely'' by a slightly smaller amount than the global average. Projected changes in rainfall over South East Asia vary, depending on model, sub-region and season ( ''high confidence'' ), with consistent projections of increases in annual mean rainfall from CMIP5 and CMIP6 over most land areas ( ''medium confidence'' ) and decreases in summer rainfall from CORDEX projections over much of Indonesia ( ''medium confidence'' ).

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=== Atlas.5.5 South West Asia ===

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<span id="atlas.5.5.1-key-features-of-the-regional-climate-and-findings-from-previous-ipcc-assessments"></span>
==== [[#Atlas.5.5.1|Atlas.5.5.1]] Key Features of the Regional Climate and Findings From Previous IPCC Assessments ====

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<span id="atlas.5.5.1.1-key-features-of-the-regional-climate"></span>
===== Atlas.5.5.1.1 Key Features of the Regional Climate =====

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South West Asia includes the Arabian Peninsula (ARP) and West Central Asia (WCA) reference regions (Figure Atlas.1 7). ARP has a semi-arid or arid desert climate with very low annual mean precipitation and very high temperature. Its temperature is influenced by SST variations over the tropical ocean (e.g., ENSO) and the NAO and AO (see [[IPCC:Wg1:Chapter:Annex-iv|Annex IV]] for these and subsequent modes of variability; [[#Attada--2019|Attada et al., 2019]] ). Rainfall is influenced by the IOD and ENSO, with more rainfall during El Niño ( [[#Kang--2015|Kang et al., 2015]] ; [[#Kumar--2015|Kumar et al., 2015]] ; [[#Abid--2018|Abid et al., 2018]] ; [[#Kamil--2019|Kamil et al., 2019]] ) and less during La Niña ( [[#Atif--2020|Atif et al., 2020]] ). The wet season in ARP is mainly from November to April and the dry season is from June to August. Rainfall is confined mostly to the south-western part of the peninsula and contribution of extreme events to the total rainfall varies within 20–70% from region to region and season to season ( [[#Almazroui--2020b|Almazroui, 2020b]] ; [[#Almazroui--2020|Almazroui and Saeed, 2020]] ). WCA is separated from Eastern Europe by the Caucasus Mountains, is adjacent to ARP, with South Asia (SAS) to the south and West Siberia (WSB) to the north, and lies between the Mediterranean (MED), Tibetan Plateau (TIB) and East Central Asia (ECA) regions. WCA is heterogeneous in terrain with the Zagros Mountains and Iranian Plateau in the west and south-west, the Caspian Sea and lowland with deserts in the north and north-east. The regional climate of WCA is influenced by the NAO and ENSO and it is typically semi-arid or arid with a strong gradient in both precipitation and temperature from the mountains to the plains and from north to south.

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===== Atlas.5.5.1.2 Findings From Previous IPCC Assessments =====

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The IPCC AR5 established it is ''very likely'' that temperatures will continue to increase over WCA in all seasons whilst projections of decreased annual mean precipitation had ''medium confidence'' due to ''medium agreement'' resulting from model-dependent sub-regional and seasonal changes ( [[#Christensen--2013|Christensen et al., 2013]] ). The AR5 also concluded that for a better understanding of the climate of the region, results of high-resolution regional climate models also need to be assessed and CMIP5 models generally had difficulties simulating the mean temperature and precipitation climatology for South West Asia. This is partly related to the poor spatial resolution of the models not resolving the complex mountainous terrain and the influence of different drivers of the European, Asian and African climates. However, observational data scarcity and issues related to the comparison of observations with coarse-resolution models added to the uncertainty and remained poorly analysed in peer-reviewed literature on climate model performance ( [[#Christensen--2013|Christensen et al., 2013]] ).

The SR1.5 stated that even for 1.5°C and 2°C of global warming, South West Asia is among the regions with the strongest projected increase in hot extremes with more urban populations exposed to severe droughts in West Asia, while an increase of heavy precipitation events is projected in mountainous regions of Central Asia ( [[#Hoegh-Guldberg--2018|Hoegh-Guldberg et al., 2018]] ; [[#IPCC--2018c|IPCC, 2018c]] ). Higher temperatures with less precipitation will ''likely'' result in higher risks of desertification, wildfires and dust storms exacerbated by land-use and land-cover changes in the region with consequent effects on human health. Further drying of the Aral Sea in Central Asia will ''likely'' have negative effects on the regional microclimate, adding to the growing wind erosion in adjacent deltaic areas and deserts that is already resulting in a reduction of the vegetation productivity including croplands. There is also a projected increase of precipitation intensity in the Arabian Peninsula which is ''likely'' to lead to higher soil erosion particularly in winter and spring due to floods ( [[#Mirzabaev--2019|Mirzabaev et al., 2019]] ). WCA includes high mountains with enhanced warming above 500 m where, regardless of the emissions scenario, decreases in snow cover are projected due to increased winter snowmelt and more precipitation falling as rain ( ''high confidence'' ). A very strong interannual and decadal variability, as well as scarce in situ records for mountain snow cover, have prevented a quantification of recent trends in High Mountain Asia (Hock et al., 2019b).

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==== [[#Atlas.5.5.2|Atlas.5.5.2]] Assessment and Synthesis of Observations, Trends and Attribution ====

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Since AR5, there has been an increasing number of studies on past climate change in South West Asia though meteorological stations are sparsely scattered in the region. They are mainly located in the plains below 2 km of altitude, very scarce in mountainous areas and have declined in number in WCA since the end of the Soviet Union in 1991. This increases the uncertainty in both temperature and precipitation trends, particularly for elevated areas ( ''high confidence'' ) ( [[#Christensen--2013|Christensen et al., 2013]] ; [[#Huang--2014|Huang et al., 2014]] ). So researchers use other sources of climate data in the region, particularly freely available gridded data (Annex I).

Globally, drylands showed an enhanced warming over the past century of 1.2°C–1.3°C, significantly higher than the warming over humid lands (0.8°C–1.0°C) (J. [[#Huang--2017|]] [[#Huang--2017|Huang et al., 2017]] ). A strong increase in annual surface air temperature of 0.27°C–0.47°C per decade has been found over WCA between 1960 and 2013 ( ''very high confidence'' ) ( [[#Han--2013|Han and Yang, 2013]] ; [[#Li--2013|Li et al., 2013]] ; [[#Hu--2014|Hu et al., 2014]] , 2017; [[#Huang--2014|Huang et al., 2014]] ; [[#Deng--2017|Deng and Chen, 2017]] ; [[#Zhang--2017|Zhang et al., 2017]] , 2019a; H. [[#Guo--2018|]] [[#Guo--2018|Guo et al., 2018]] ; [[#Haag--2019|Haag et al., 2019]] ; [[#Yu--2019|Yu et al., 2019]] ) ''.'' Warming is most prominent in the spring based on the CRU dataset with rates ''likely'' ranging from 0.64°C–0.82°C per decade ( [[#Hu--2014|Hu et al., 2014]] ). Analysis of seasonal temperature trends based on high-resolution 1 km × 1 km downscaled dataset CHELSA and 20 stations in Uzbekistan has confirmed the maximum significant trend in temperature from 0.6°C up to 1°C per decade in spring from 1979 to 2013 and no significant trend in winter ( [[#Khaydarov--2019|Khaydarov and Gerlitz, 2019]] ). There is ''very high confidence'' ( ''robust evidence'' , ''high agreement'' ) that the shrinking of the Aral Sea has induced an increase in surface air temperature around the Aral Sea region in the range of 2°C–6°C ( [[#Baidya%20Roy--2014|Baidya Roy et al., 2014]] ; [[#McDermid--2017|McDermid and Winter, 2017]] ; [[#Sharma--2018|Sharma et al., 2018]] ). The plateau of Iran has experienced significant increases in the average monthly values of daily maximum and minimum temperatures with spatially varying rates of 0.1°C–0.3°C up to 0.3°C–0.4°C per decade and greater spatial variation in minimum temperatures ( ''high confidence'' ) ( [[#Mahmoudi--2019|Mahmoudi et al., 2019]] ; [[#Fathian--2020|Fathian et al., 2020]] ; [[#Sharafi--2020|Sharafi and Mir Karim, 2020]] ).

Observed warming over northern ARP is higher than over the south, where minimum temperatures are increasing faster than maximum temperatures ( [[#Almazroui--2020a|Almazroui, 2020a]] ). The rate of mean temperature increase is estimated at 0.10°C per decade over 1901–2010 ( [[#Attada--2019|Attada et al., 2019]] ), while it has reached 0.63°C ( ''likely'' in the range of 0.24°C–0.81°C) per decade for the more recent period of 1978–2019 ( [[#Almazroui--2020a|Almazroui, 2020a]] ).

An overall increasing trend of annual precipitation (0.66 mm per decade) was found over Central Asia based on GPCC v7 data for the period 1901–2013 ( [[#Hu--2017|Hu et al., 2017]] ), but annual trends were found not significant over the shorter period 1960–2013 (Figure Atlas.11 and Interactive Atlas). Winter precipitation saw a significant increase of 1.1 mm per decade ( [[#Song--2016|Song and Bai, 2016]] ). These estimates have ''low'' to ''medium confidence'' since the satellite precipitation products have large systematic and random errors in mountainous regions. Moreover CMORPH and TRMM products fail to capture the precipitation events in the ice/snow covered regions in winter and show a substantial false-alarm percentage in summer, but the gauge-corrected GSMAP performs better than other products ( [[#Song--2016|Song and Bai, 2016]] ; [[#Guo--2017b|Guo et al., 2017b]] ; [[#Hu--2017|Hu et al., 2017]] ; S. [[#Chen--2019|]] [[#Chen--2019|Chen et al., 2019]] ). Over the elevated part of eastern WCA precipitation increases in the range of 1.3–4.8 mm per decade during 1960–2013 were observed ( ''very high confidence'' ) ( [[#Han--2013|Han and Yang, 2013]] ; [[#Li--2013|Li et al., 2013]] ; [[#Hu--2014|Hu et al., 2014]] , 2017; [[#Huang--2014|Huang et al., 2014]] ; [[#Deng--2017|Deng and Chen, 2017]] ; [[#Zhang--2017|Zhang et al., 2017]] , 2019a; H. [[#Guo--2018|]] [[#Guo--2018|Guo et al., 2018]] ; [[#Haag--2019|Haag et al., 2019]] ; [[#Yu--2019|Yu et al., 2019]] ). Reductions in spring precipitation and increases in winter have been reported for Uzbekistan over the period 1979–2013 based on station data but these are not significant ( [[#Khaydarov--2019|Khaydarov and Gerlitz, 2019]] ). There is ''very low confidence'' of the impact of the Aral Sea shrinking on precipitation ( [[#Chen--2011|Chen et al., 2011]] ; [[#Jin--2017|Jin et al., 2017]] ).

A decreasing trend of precipitation is reported for ARP with the mean value of –6.3 mm per decade (range of –30 mm–16 mm) for the period 1978–2019 ( ''low confidence'' ) with large interannual variability over Saudi Arabia, which covers 80% of the region ( [[#AlSarmi--2011|AlSarmi and Washington, 2011]] ; [[#Almazroui--2012|Almazroui et al., 2012]] ; [[#Donat--2014|Donat et al., 2014]] ). The same decreasing trend in precipitation totals and an increasing trend in the number of consecutive dry days are found for most of the Iranian Plateau ( ''medium confidence'' ) ( [[#Rahimi--2019|Rahimi and Fatemi, 2019]] ; [[#Fathian--2020|Fathian et al., 2020]] ; [[#Sharafi--2020|Sharafi and Mir Karim, 2020]] ). January-to-March mean snow cover and depth over mountainous areas decreased between 2000 and 2019 ( ''low'' to ''medium confidence'' due to ''limited evidence'' ) ( [[#Safarianzengir--2020|Safarianzengir et al., 2020]] ).

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==== [[#Atlas.5.5.3|Atlas.5.5.3]] Assessment of Model Performance ====

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There is ''limited evidence'' about the performance of GCMs and RCMs in representing the current climate of South West Asia due to very few studies evaluating models over this region, but literature is now emerging particularly on CMIP5/CMIP6 and CORDEX simulations.

Over ARP, surface temperature biases for 18 of 30 CMIP5 models are within one standard deviation of the observed variability ( [[#Almazroui--2017|Almazroui et al., 2017]] ). A warm bias in summer and a cold bias for other months along with an underestimation of wet-season precipitation and an overestimation in the dry season have been reported in 26 CMIP5 models ( [[#Lelieveld--2016|Lelieveld et al., 2016]] ). Thirty CMIP6 GCMs have limited skill in simulating annual precipitation patterns, annual cycle statistics and long-term precipitation trends over Central Asia partially due to considerable wet biases of up to 100% in the southern Xinjiang and Hexi Corridor regions ( [[#Guo--2021|Guo et al., 2021]] ). Also, CMIP6 models display a wide range of performance in reproducing ENSO teleconnections that influence the region ( [[#Barlow--2021|Barlow et al., 2021]] ).

RCM simulations using the CORDEX-MENA domain reproduce the main features of the mean surface climatology over ARP with moderate biases ( ''high confidence'' ). RegCM4 driven by five GCMs (HadGEM2, GFDL, CNRM, CanESM2 and ECHAM6) showed an ensemble-mean cold bias of about –0.7°C and a dry bias of –13% over ARP ( [[#Almazroui--2016|Almazroui, 2016]] ) with a cold (warm) bias over western (south-eastern) areas ( [[#Syed--2019|Syed et al., 2019]] ). Temperature biases in 30-year historical simulations with WRF using three different radiation parametrizations were within ±2°C and mostly caused by surface long-wave radiation errors which affected nighttime minimum temperatures over 70% of the domain ( [[#Zittis--2017|Zittis and Hadjinicolaou, 2017]] ). Mean absolute errors in COSMO-CLM driven by ERA-Interim were about 1.2°C for temperature, 15 mm per month for precipitation and 9% for total cloud cover, and with new parametrizations of albedo and aerosols optimized for the region the RCM simulated the main climate features of this very complex area ( [[#Bucchignani--2016|Bucchignani et al., 2016]] ). RegCM4.4 also simulated the main features of the observed climatology (especially for dry regions) with temperature biases within ±3.0°C. Annual precipitation was overestimated with winter and spring underestimated ( [[#Ozturk--2018|Ozturk et al., 2018]] ).

Four RCMs (REMO, RegCM4.3.5, ALARO-0, and COSMO-CLM5.0) driven by ERA-Interim, NCEP2 reanalyses and two different GCMs reproduced reasonably well the spatio-temporal patterns for temperature and precipitation though underestimated diurnal temperature range and had cold biases over mountainous and high plateau regions in all seasons. There is ''low confidence'' in this result because of low station density and a lack of high-elevation stations, and with biases dependent on the choice of the observational dataset. However, the performance of both GCMs and RCMs is better than reanalyses when compared to available observations ( [[#Mannig--2013|Mannig et al., 2013]] ; [[#Ozturk--2017|Ozturk et al., 2017]] ; [[#Russo--2019|Russo et al., 2019]] ; [[#Top--2021|Top et al., 2021]] ).

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==== [[#Atlas.5.5.4|Atlas.5.5.4]] Assessment and Synthesis of Projections ====

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Temperature and precipitation projections from CMIP5/CMIP6 and CORDEX for different GWLs, SSP and RCP scenarios, time periods and baselines are shown in Figure Atlas.1 7 and further details can be explored in the Interactive Atlas.

In WCA, projections for different GWLs are consistent not only in annual and seasonal warming but in the ranges of the projections. Under RCP8.5, annual mean temperature will ''likely'' exceed 2°C by mid-century (compared with 1995–2014) and reach up to 4.8°C–6°C by the end of the century ( [[#Yang--2017|Yang et al., 2017]] ), with faster warming projected by the CMIP6 ensemble under SSP5-8.5. In individual county-level studies on GCM future climate projections, temperatures increased by up to 7°C by the end of the century, depending on season and emissions scenario ( [[#Allaberdiyev--2010|Allaberdiyev, 2010]] ; [[#MENRPG--2015|MENRPG, 2015]] ; [[#MNP--2015|MNP, 2015]] ; [[#Gevorgyan--2016|Gevorgyan et al., 2016]] ; [[#Osborn--2016|Osborn et al., 2016]] ; [[#Aalto--2017|Aalto et al., 2017]] ; [[#IDOE--2017|IDOE, 2017]] ; [[#Salman--2017|Salman et al., 2017]] ). Statistical downscaling of 18 CMIP5 GCMs projected an annual temperature increase of 0.37°C per decade (under RCP4.5) with the maximum in northern WCA and warming most conspicuous in summer ( [[#Luo--2019|Luo et al., 2019]] ). RCM downscaling of GCMs over Central Asia projected a larger increase of temperature under RCP8.5 for the 2071–2100 period, ranging from 5°C to 8°C ( [[#Ozturk--2017|Ozturk et al., 2017]] ).

In ARP, the projected change in ensemble mean annual temperature from 30 CMIP6 models is from 1.6°C (SSP1-2.6) to 5.3°C (SSP5-8.5) by 2070–2099 compared to 1981–2010 ( [[#Almazroui--2020a|Almazroui et al., 2020a]] ). The projected warming is the highest in the north, reaching 5.9°C and lowest in the south (4.7°C). COSMO-CLM projections over the CORDEX-MENA domain show for ARP and WCA a strong warming with marked seasonality for the end of the 21st century, ranging from 2.5°C in winter under RCP4.5 to 8°C in summer under RCP8.5 and with large increases found over high-altitude areas in winter and spring ( [[#Bucchignani--2018|Bucchignani et al., 2018]] ; [[#Ozturk--2018|Ozturk et al., 2018]] ). The CMIP5 multi-model mean warming in boreal summer in 2070–2099, compared with 1951–1980, is projected to be about 2.5°C and 6.5°C at the 2°C and 4°C global warming levels respectively ( [[#Huang--2014|Huang et al., 2014]] ).

Future projections of precipitation in South West Asia have large uncertainties and thus ''low confidence'' . There are few significant changes, little consensus on the sign and with a tendency for reduction in CMIP5 being reversed in CMIP6 across all warming levels ( [[#Ozturk--2018|Ozturk et al., 2018]] ). Statistical downscaling of 18 CMIP5 GCMs under RCP4.5 projected an increase in precipitation of 4.6 mm per decade in South West Asia during 2021–2060 relative to 1965–2004 ( [[#Luo--2019|Luo et al., 2019]] ). CMIP5 simulations project a general decrease in precipitation over lowlands in Turkey, Iran, Afghanistan and Pakistan ( [[#Ozturk--2017|Ozturk et al., 2017]] ), and an increase over high-mountain regions ( [[#Aalto--2017|Aalto et al., 2017]] ; [[#Salman--2018|Salman et al., 2018]] ). At a 4°C global warming level, the multi-model mean annual precipitation for Turkmenistan and parts of Tajikistan and Uzbekistan is projected to decrease by 20%, with somewhat stronger relative decreases in summer ( [[#Reyer--2017|Reyer et al., 2017]] ). Over northern WCA, the CMIP5 ensemble mean projects increases of over 3 mm per decade under RCP2.6 and over 6 mm per decade under RCP4.5 and RCP8.5 over the 21st century ( [[#Huang--2014|Huang et al., 2014]] ). Mean annual precipitation is projected to rise by 5.2% at the end of the 21st century (2070–2099) under RCP8.5, compared to 1976–2005, while mean annual snowfall is projected to decrease by 26.5% in Central Asia ( [[#Yang--2017|Yang et al., 2017]] ). However, regardless of the sign of the precipitation change in the high-mountain regions of Central Asia, the influence of the warming on the snowpack will ''very likely'' cause important changes in the timing and amount of the spring melt ( [[#Diffenbaugh--2013|Diffenbaugh et al., 2013]] ).

In ARP, the projected change in ensemble mean annual precipitation from 30 CMIP6 models ranges from 3.8% (–2.6 to 28.8%) to 31.8% (12.0–106.5%) under SSP1-2.6 and SSP5-8.5 emissions for the period 2080–2100 compared with 1995–2014 ( [[#Almazroui--2020a|Almazroui et al., 2020a]] ). North-west ARP precipitation is projected to decrease between –6 to –27% per decade and in the south precipitation to increase by up to 8.6% per decade. CMIP6 projections are in line with those from CMIP3 and CMIP5, however they are less variable in the central area in CMIP6. The uncertainty associated with precipitation over ARP is large because of very low annual amounts and high variability.

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==== [[#Atlas.5.5.5|Atlas.5.5.5]] Summary ====

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Increases in annual surface air temperature over South West Asia are ''very likely'' in the range of 0.24°C–0.81°C per decade over the last 50–60 years. Annual precipitation change over ARP since 1970 is estimated at –6.3 mm per decade (and in the range of –30 to 16 mm per decade) and over WCA is generally not significant except over the elevated part of eastern WCA where increases between 1.3 mm and 4.8 mm per decade during 1960–2013 have been observed ( ''very high confidence'' ). In mountainous areas, the scarcity and decline of the number of observation sites since the end of the former Soviet Union in 1991 increase the uncertainty of the long-term temperature and precipitation estimates ( ''high confidence'' ).

Mean temperature biases in RCMs are within ±3°C in South West Asia, and annual precipitation biases are positive in almost all parts of the region, except over the ARP where they are negative in the wet season (November to April) and over WCA in winter and spring (from December to May) ( ''medium confidence'' ). Since regional model evaluation literature has only recently emerged there is ''medium evidence'' about the performance of RCMs in South West Asia though with ''medium'' to ''high agreement'' on mean temperature and precipitation biases. RCMs simulate colder temperatures than observed over mountainous and high plateau regions ( ''limited evidence'' , ''high agreement'' ).

Further warming over South West Asia is projected in the 21st century to be greater than the global average, with rates varying from 0.25°C to 0.8°C per decade depending on the season and scenario, and the maximum rates found in the northern part of the region in summer ( ''high confidence'' ). The influence of the warming on the snowpack will ''very likely'' cause changes in the timing and amount of the spring melt. CMIP6 projected changes in annual precipitation totals are in the range of –3 to 29% (SSP1-2.6) and 12–107% (SSP5-8.5) in ARP ( ''medium confidence'' ). Strong spatio-temporal differences with overall precipitation decreases are projected in the central and northern parts of WCA in summer (JJA) with increases in winter (DJF) ( ''medium confidence'' ).

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