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The uneven changes in potential evapotranspiration (PET) in response to temperature rise are called the 'evapotranspiration paradox' phenomenon, which is expected to intensify further under a warming climate. In this paper, we explored the spatial–temporal changes in the future 'evapotranspiration paradox' phenomenon over China and its 10 major river sub‐regions under different climate change scenarios. Thus, this paper uses four global climate model outputs under seven shared socioeconomic pathway‐based scenarios (SSP1‐1.9, SSP1‐2.6, SSP2‐4.5, SSP3‐7.0, SSP4‐3.4, SSP4‐6.0 and SSP5‐8.5) from the sixth phase of the Coupled Model Intercomparison Project (CMIP6). Considering the latest IPCC's 6th Assessment Report (AR6), this research emphasizes the 2021–2040 (near‐term), 2041–2060 (mid‐term) and 2081–2100 (long‐term) periods to anticipate the 'evapotranspiration paradox' phenomenon. In this study, PET is estimated based on the modified Penman–Monteith (P‐M) method (considering CO2). Furthermore, the paradox phenomenon in this study is defined considering two pivotal conditions: the surface temperature increases but the evaporation decreases (Type I), and the temperature decreases but the evaporation still tends to increase (Type II). The results show that there were only Type I 'evapotranspiration paradoxes' that existed in the historical period, which were dominant especially before the 1990s. Nearly 50% of the areas experienced the Type I 'evapotranspiration paradox' phenomenon that occurred during 1975–1994 and 1995–2014. Spatially, it covered 100% of the area of the Southeast River (SER) and the Liaohe River (LR) during 1975–1994 and the area of the SER, the HAR, the HHR and the LR during 1995–2014. In the future, the interdecadal growth rate of PET in China is projected to be the highest under the SSP5‐8.5 and the lowest under the SSP3‐7.0 with spatial variation. Importantly, the largest areas of approximately 36% and 45% with the Type I phenomenon are inclined to occur under the SSP1‐1.9 and SSP4‐6.0, respectively, over the long‐term period (2081–2100). The area with the Type I phenomenon will be less than 20% in the near‐term, and it is less than 12% in the mid‐term period. For the Type II evapotranspiration paradox, the uppermost 45% of the area is expected to experience the Type II phenomenon under SSP1‐1.9 during the mid‐term period, while it is 30% under SSP1‐2.6 during the long‐term period. However, this study's findings provide the scientific basis for formulating adaptation and mitigation strategies to combat 'evapotranspiration paradox'‐related extremes at regional scales. [ABSTRACT FROM AUTHOR]

In recent years, concurrent climate extreme conditions (i.e., hot‐dry, cold‐dry, hot‐wet, and cold‐wet) have led to various unprecedented natural disasters (e.g., floods, landslide, wildfire, droughts, etc.), causing significant damages to human societies and ecosystems. This is especially true for China where many unprecedented natural disasters have been reported due to the recent warming in local climate. In this paper, we focus on the issue of ultra‐extreme events (1‰ threshold) and address how future global warming would affect the climate extreme conditions in China. Specifically, to reduce the uncertainties from models, we use a downscaled and bias‐corrected CMIP6 ensemble under two continuously‐warming scenarios to evaluate the impact of global warming on ultra‐extreme events over China. The results show that, under both SSP245 and SSP585 scenarios, extreme hot conditions would become dominant in most regions of China and some regions are likely to experience over 50 extreme hot days at future warming levels. The frequency of extreme cold events is projected to be small. More frequent extreme hot‐wet events with concurrence in the same month and year would be expected for China under the continuously‐warming scenarios. This is particularly obvious for the west where more than 6 hot‐wet months are likely to take place under future warming scenarios. This may imply that more extreme heat waves and flooding events would coincide in the same month or year for China in the future. For univariate ultra‐extreme events, both extreme hot events and extreme wet events would drop by above 25% from 2.0°C to 1.5°C global warming level, particularly under the SSP245 scenario. When the global mean temperature is limited to 1.5°C rather than 2°C, the avoided impacts of hot‐wet and cold‐wet extremes concurring in the same month will be larger than those of dry‐related compound extremes. Overall, the results suggest that slowing down global warming can reduce the frequency of concurrent climate extreme conditions in China, highlighting the importance of immediate action toward carbon emission reduction. Plain Language Summary: In recent years, concurrent climate extreme conditions (e.g., hot‐dry, cold‐dry, hot‐wet, and cold‐wet) have led to various unprecedented natural disasters (e.g., floods, landslide, wildfire, droughts, etc.), causing significant damages to human societies and ecosystems. This is especially true for China where many unprecedented natural disasters have been reported due to the recent warming in local climate. In this paper, we focus on the issue of ultra‐extreme events (1‰ threshold) and address how future global warming would affect the climate extreme conditions in China. Here, we use a downscaled and bias‐corrected CMIP6 ensemble under two continuously‐warming scenarios to address this question. The results show that, under both SSP245 and SSP585 scenarios, extreme hot conditions would become dominant in most regions of China and some regions are likely to experience over 50 extreme hot days at future warming levels. Both extreme hot events and extreme wet events would drop by above 25% from 2.0°C to 1.5°C global warming level, particularly under the SSP245 scenario. Overall, the results suggest that slowing down the global warming can reduce the frequency of concurrent climate extreme conditions in China, highlighting the importance of immediate action toward carbon emission reduction. Key Points: A downscaled and bias‐corrected CMIP6 ensemble is used to evaluate the impact of global warming on ultra extreme events over ChinaMore frequent extreme hot‐wet events with concurrence in the same month and year would be expected for ChinaBoth extreme hot events and extreme wet events would drop by above 25% from 2.0°C to 1.5°C global warming level [ABSTRACT FROM AUTHOR]

With the growing share of the climate-sensitive renewable energy market supply in China, estimating future energy drought risk (ED) is essential. In this paper, based on CMIP6, energy demand and power generation are estimated from 2023 to 2100 under SSP2-4.5 and SSP5-8.5 climate scenarios, and ED is defined from high demand (HD), low wind-solar generation (LWS), and residual load (RL). The results show that the ED exacerbation is relatively high under the SSP5-8.5. Between 2023 and 2100, the duration and severity of HD and RL notably increase, while LWS changes are relatively weak. We find that HD and RL exhibit high summer risks in the south and high winter risk in the north. The return periods vary significantly and extends over time in most regions. Moreover, there are few ED when the installed power generation capacity (IPGC) is increased. When the IPGC increases by 2%–5%, the LWS will decrease at least 20%–72%. With a 10% increase, the LWS will be mitigated by at least 80%, whereas the mitigation for RL is limited, approximately 3–17% at different rates. This work performs the first evaluation of future ED and provides the useful references for risk mitigation in China based on CMIP6. [ABSTRACT FROM AUTHOR]

Broussonetia papyrifera is an important native tree species in China with strong adaptability, wide distribution and economic importance. Climate change is considered as the main threat to ecological processes and global biodiversity. Predicting the potential geographical distribution of B. papyrifera in future climate change scenarios will provide a scientific basis for ecological restoration in China. Principal component analysis and Pearson correlation analysis were conducted to select the environmental variables. The distribution and changes in the potential suitable area for B. papyrifera were predicted using the maximum entropy model and the CIMP6 dataset from 2041 to 2060. The current highly suitable areas for B. papyrifera were mainly located in Guangdong (5.60 × 104 km2), Guangxi (4.39 × 104 km2), Taiwan (2.54 × 104 km2) and Hainan (2.17 × 104 km2). The mean temperature of the coldest quarter (11.54–27.11 °C), precipitation of the driest quarter (51.48–818.40 mm) and precipitation of the wettest quarter (665.51–2302.60 mm) were the main factors limiting the suitable areas for B. papyrifera. The multi-modal average of the highly and the total suitable areas for B. papyrifera were 111.42 × 104 and 349.11 × 104 km2 in the SSP5-8.5 scenario, while those in the SSP1-2.6 scenario were 87.50 × 104 and 328.29 × 104 km2, respectively. The gained suitable areas for B. papyrifera will expand to the western and northern China in the future scenarios. The multi-model averaging results showed that the potential available planting area was 212.66 × 104 and 229.32 × 104 km2 in the SSP1-2.6 and SSP5-8.5 scenarios, respectively, when the suitable area within the farmland range was excluded. [ABSTRACT FROM AUTHOR]

Understanding future dry and wet changes under global climate change is of great regional significance in China. Based on six Global Climate Models (GCM) from the Coupled Model Intercomparison Project phase 6 (CMIP6), we project the changes of dry‐wet climate regions in China in the 21st century under the three SSP‐RCP (shared socioeconomic pathway‐representative concentration pathway) scenarios (SSP1‐2.6, SSP2‐4.5 and SSP5‐8.5), and further identify the key climatic factors that lead to the transition of dry‐wet climate regions. The results show the area of humid and subhumid regions in China will show an upward trend in the future, while the area of semiarid and arid regions will show a downward trend. Especially under the SSP5‐8.5 scenario, the area of the subhumid region has the largest increase trend (0.042% per year), and the area of the arid region has the largest decline trend (−0.048% per year). The three main transfer directions of dry‐wet climate regions are from arid to semiarid regions, from semiarid to subhumid regions, and from subhumid to humid regions in the future. Due to the future extensive wetting trend in the north, the future dry‐wet climate regions will shift to wetter types in Tibet, North China, the Qinling‐Huaihe regions and the northeast. In addition, the key climatic factors for the transition of dry‐wet climate regions are mainly precipitation, maximum temperature and solar radiation. The increase of precipitation plays a dominant role for areas projected to change to wetter types, and the increase of potential evapotranspiration (PET) plays a dominant role for areas projected to change to drier types. The PET changes in changed areas of dry‐wet climate regions are mainly dominated by maximum temperature and solar radiation. Our research has important implications for future water resource management and reconsideration of climate mitigation and adaptation policies. [ABSTRACT FROM AUTHOR]

Diurnal temperature range (DTR) is an important meteorological component affecting the yield and protein content of winter wheat. The accuracy of climate model simulations of DTR will directly affect the prediction of winter wheat yield and quality. Previous model evaluations for worldwide or nationwide cannot answer which model is suitable for the estimation of winter wheat yield. We evaluated the ability of the coupled model intercomparison project phase 6 (CMIP6) models to simulate DTR in the winter wheat–growing regions of China using CN05 observations. The root mean square error (RMSE) and the interannual variability skill score (IVS) were used to quantitatively evaluate the ability of models in simulating DTR spatial and temporal characteristics, and the comprehensive rating index (CRI) was used to determine the most suitable climate model for winter wheat. The results showed that the CMIP6 model can reproduce DTR in winter wheat–growing regions. BCC-CSM2-MR simulations of DTR in the winter wheat–growing season were more consistent with observations. EC-Earth3-Veg simulated the climatological DTR best in the wheat growing regions (RMSE = 0.848). Meanwhile, the evaluation for climatological DTR in China is not applicable to the evaluation of DTR in winter wheat–growing regions, and the evaluation for annual DTR is not a substitute for the evaluation of winter wheat–growing season DTR. Our study highlights the importance of evaluating winter wheat–growing regions' DTR, which can further improve the ability of CMIP6 models simulating DTR to serve the research of climate change impact on winter wheat yield. [ABSTRACT FROM AUTHOR]

The present study aimed to evaluate the performance of 46 global climate models (GCMs) from the newly released Coupled Model Intercomparison Project Phase 6 (CMIP6) in the historical simulation of precipitation and temperature, and select the best performing GCMs for future projection across China and three major river basins. This study uses four shared socioeconomic pathways (SSPs), namely SSP1‐2.6, SSP2‐4.5, SSP3‐7.0, and SSP5‐8.5 relative to the base period (1961–2014). Initially, 46 models were evaluated across China employing an improved Taylor diagram method. Based on relatively better performance, 10 best‐performing models (TBMs) were selected out of 46 models for further evaluation. The results show that historical temperature was well reproduced by CMIP6 over the study regions with a high correlation coefficient (CC). All the TBMs produced good CC ranging from 0.8 to 0.99 presenting the precipitation and temperature distribution well. Meanwhile, EC‐Earth3 and EC‐Earth3‐Veg well simulated the precipitation and temperature amounts as well as trends over selected three river basins. The multimodel ensemble mean (MME) underestimates temperature over China and selected three basins with bias values of −0.53, −0.21, −0.91, and −0.68°C, respectively. In contrast, MEM overestimated the simulated precipitation with the amount of 27.7, 32.4, 21.0, and 104.6% across China and selected three basins. During future projections, increased precipitation and temperature trends are projected over three selected river basins as well as all across China. The increasing trend of future precipitation over China under SSP1‐2.6, SSP2‐4.5, SSP3‐7.0, and SSP5‐8.5 scenarios are 0.65, 0.86, 1.29, and 0.76 mm·a−1, whereas, the increasing trend of temperature is 0.008, 0.028, 0.050, and 0.065°C·a−1, respectively. Comparatively, the greater the radiation force, the higher projected increases in precipitation and temperature across China and three major river basins were observed. The extent of CMIP6 models over the target region and its river basins calls for further deep assessment of the attribution and possible implementation of robust methods that can accurately simulate the observed patterns for future practice. [ABSTRACT FROM AUTHOR]

In recent years, China has suffered from frequent extreme precipitation events, and predicting their future trends has become an essential part of the current research on this issue. Because of the inevitable uncertainties associated with individual models for climate prediction, this study uses a machine learning approach to integrate and fit multiple models. The results show that the use of several evaluation metrics provides better results than the traditional ensemble median method. The correlation coefficients with the actual observations were found to improve from about 0.8 to 0.9, while the correlation coefficients of the precipitation amount (PRCPTOT), very heavy precipitation days (R20mm), and extreme precipitation intensity (SDII95) reached 0.95. Based on this, the precipitation simulations of moderate forced scenario for sharing socio-economic path (SSP2-4.5) from 27 coupled models in the Coupled Model Intercomparison Project Phase 6 (CMIP6) were used to explore potential changes in future extreme precipitation events in China and to calculate the distribution and trends of the PRCPTOT, extreme precipitation amount (R95pTOT), maximum consecutive 5-day precipitation (Rx5day), precipitation intensity (SDII), SDII95, and R20mm for the early 21st century (2023–2050), mid-21st century (2051–2075), and late 21st century (2076–2100), respectively. The results showed that the most significant increase in extreme precipitation indices is expected to occur by the end of the century, with the R95pTOT, Rx5day, and SDII95 increasing by 13.73%, 9.43%, and 9.34%, respectively, from the base period. The remaining three precipitation indexes, the PRCPTOT, SDII, and R20mm, also showed increases of 8.77%, 6.84%, and 4.02%, respectively. Additionally, there were apparent differences in the spatial variation of extreme precipitation. There were significant increasing trends of extreme precipitation indexes in central China and northeast China in the three periods, among which the total annual precipitation showed an increasing trend in central and northern China and a decreasing trend in western and south China. An increasing trend of annual precipitation intensity was found to be mainly concentrated in central China and south China, and the annual precipitation frequency showed a larger increasing trend at the beginning of this century. The annual precipitation frequency showed an increasing trend in the early part of this century. In general, all the indices showed an overall increasing trend in the future period, with the PRCPTOT, Rx5day, and SDII95 showing the most significant overall increasing trends. [ABSTRACT FROM AUTHOR]

Performance of six models in the Coupled Model Intercomparison Project phase 5 (CMIP5) and their new versions in CMIP phase 6 (CMIP6) in representing the climatological (1976–2005) precipitation extremes over China were evaluated based on five precipitation indices. Improvements are found in CMIP6 models in simulating the climatology of all five indices, in which GFDL‐CM4 and GFDL‐ESM4 show significant improvement. Dry biases over South China (SC) are reduced in five CMIP6 models (BCC‐CSM, CanESM, GFDL‐CM, GFDL‐ESM, and IPSL‐CM), with the largest decreased root‐mean‐square error (RMSE) of 59.2% in GFDL‐CM. The reduced dry biases in CMIP6 can be attributed to more moisture transported into SC from the southern boundary and dynamic processes of the atmosphere except in BCC‐CSM, where the increased evaporation dominates. Additionally, increased heavy precipitation events (>20 mm·day−1) are produced over SC in CMIP6 models. Wet biases over West China (WC) are also reduced. with the largest reduced RMSE of 46.8% in GFDL‐CM, which are related to the reduced precipitation frequency (more than 40%) and weakened precipitation intensity. In addition, the CMIP6 models show a higher skill in simulating the frequency distribution of daily precipitation intensity. More heavy precipitation over SC and Northeast China, and fewer weak precipitation (<20 mm·day−1) over WC can be reasonably reproduced. Although the CMIP6 models have obviously improved in simulating total precipitation on wet days, wet days (WD), simple daily intensity index (SDII), and extreme precipitation amount (R95T), the bias still exists in simulating consecutive dry days (CDD). [ABSTRACT FROM AUTHOR]

Precipitation falls unevenly in space and time, which has significant consequences for human society and natural ecosystems. Based on 2,134 gauged observations during 1961–2020 and historical simulations of 14 models participating in the Coupled Model Intercomparison Project Phase 6 (CMIP6), we analyze precipitation unevenness over China in terms of the spatiotemporal distribution of wet days (>1 mm) and wet‐day precipitation amount throughout the year. We also explore precipitation extremes related to the unevenness. Spatially, precipitation shows medium unevenness in China. Temporally, the unevenness in distribution of wet days increases from southeast (generally humid) to northwest (generally arid). This temporal unevenness weakened in large parts of western China during 1991–2020 relative to 1961–1990. As for precipitation amount, the distribution of precipitation that falls on wet days is more uneven in water‐limited regions compared with humid regions. Associated with this unevenness, between 1961 and 2020, the top 50% of annual precipitation fell in approximately the 12 wettest days, and the maximum 1‐day precipitation for the year contributed 10% on average to annual precipitation. The period 1991–2020 shows overall greater unevenness in eastern China and less unevenness in western China relative to the first 30 years. Multimodel ensemble means of CMIP6 simulations agree with the basic spatial pattern of observations, while underestimating the magnitude of temporal unevenness of precipitation. If these regional differences in precipitation unevenness were to persist over time, this would likely create some new challenges for water resource management in these areas, e.g., flash drought. Key Points: Wet‐day frequency was more spatially even in 1991–2020 compared to 1961–1990, while wet‐day precipitation was more spatially unevenTemporally, the distribution of precipitation that falls on wet days is more uneven in water‐limited regions as compared with humid regionsCMIP6 agrees with spatial pattern of observed precipitation, while underestimating the magnitude of temporal unevenness of precipitation [ABSTRACT FROM AUTHOR]

Projections of future drought conditions under climate change are an important step in formulating the long‐term climate adaptation strategies. It is therefore valuable to predict the drought conditions in China following the release of the CMIP6 (the phase six of the Coupled Model Inter‐comparison Project). Thus, based on 20 global climate model simulations from CMIP6, we project China's drought conditions and its socioeconomic impacts using the self‐calibrated Palmer Drought Severity Index (scPDSI). Four scenarios are considered in this analysis: SSP1‐2.6 (the low‐level development scenario), SSP2‐4.5 (the middle‐level development scenario), SSP3‐7.0 (the medium to high‐level development scenario) and SSP5‐8.5 (the high‐level development scenario). Under SSP1‐2.6, we observed wetting trends over large areas of China except the arid region during 2020–2099; however, under SSP2‐4.5, SSP3‐7.0 and SSP5‐8.5, significant drying trends are detected in the humid and temperate semi‐humid region, while in other areas there are significant wetting trends. The projected drought conditions are likely to be severe with more frequent monthly occurrences and higher probability of extreme drying conditions, especially in these humid and temperate semi‐humid regions under SSP3‐7.0 and SSP5‐8.5. Consequently, the population exposure to drought in most climatic regions will increase initially up to 2040s and gradually decrease under all the scenarios except SSP3‐7.0; and the humid region will be a future hotspot where the impact of climate on population exposure to drought will be more significant. The economic exposure to drought will increase over the whole China under all four scenarios, especially in the humid and semi‐humid region. Our results have important implications for future drought projections and provide a scientific evidence for developing climate change adaptation strategies and disaster prevention. [ABSTRACT FROM AUTHOR]

In this paper, future drought characteristics (frequency, duration and intensity) over China are analysed by using four climate models from CMIP6 under the seven SSP-RCP (shared socioeconomic pathway-representative concentration pathway) scenarios (SSP119, SSP126, SSP434, SSP245, SSP460, SSP370, and SSP585) for three defined periods of 2021–2040 (near-term), 2041–2060 (mid-term) and 2081–2100 (long-term). The corresponding four climate models output from CMIP5 are also used to conduct a comparison analysis between CMIP5 and CMIP6 to address the improvements added to CMIP6 in terms of drought identification. The drought characteristics are identified by applying the standardized precipitation-evapotranspiration index (SPEI) at a 12-month timescale and run theory. The results show that CMIP6 has a robust capability to capture historical (1986–2005) drought characteristics. For the future period of 2021–2040, the decrease in precipitation and increase in potential evapotranspiration will lead to continuous dry conditions in the upper and middle Yangtze River basin and eastern Pearl River basin. Relative to the reference period, drought events will be more frequent and severe with longer durations in the Northwest River basins and middle Yangtze River basin. Furthermore, higher emissions signify a greater increase in drought frequency and intensity in the long-term period. Except for the SSP585 scenario, the lower emission scenario corresponds to the higher drought duration soon and in the mid-21st century (2021–2060). This finding is regarded as a "strange phenomenon", which cannot be detected by the previous CMIP5-based emission scenarios (RCP2.6, RCP4.5 and the unlikely pathway RCP8.5). Therefore, additional "possible future"-based scenarios (SSP119, SSP126, SSP434, SSP245, SSP460, and SSP370) should be included in extreme climate studies, especially for the near future and mid-21st century. Notably, compared with CMIP5, the reduced biases in drought characteristics are more likely associated with improvements in the representation of physical processes in climate models from CMIP6. The results of this study could provide a basis for the development of drought adaptation measures over China. • Latest CMIP6 climate models and seven SSP-RCP scenarios are used to analyze the possible changes in droughts over China. • More robust projections will be presented by CMIP6 than CMIP5, due to the better representations in physical processes. • The higher emission signifies shorter drought duration, lower precipitation and PET in mid-21st century over China. [ABSTRACT FROM AUTHOR]

Precipitation fluctuations are continuously threatening the environment and may cause huge economic losses. In present study, the precipitation over China has been evaluated under five principal shared socioeconomic pathways (SSPs) scenarios during 2015–2099 based on eight CMIP6 models bias-corrected by the method of Equidistant Cumulative Distribution Functions. The results showed that (1) the simulated precipitation in China was in good agreement with observed precipitation for the eight CMIP6 models during 1961–2014, especially for the UKESM1-0-LL and MIROC6. However, the simulated annual mean precipitation has been significantly overvalued in the Southwest River basin (> 50%), while it was undervalued in the higher elevations of the Northwest River basin (< − 60%); (2) the annual mean precipitation will show a fluctuating upward trend during 2015–2099 over China under all the SSPs scenarios for the eight CMIP6 models. The rate of precipitation increase over north China will be higher than that in south China, especially in the Northwest River basin (reach 57.44% in the 2090s under the SSP5-8.5 for the ensemble mean). This increase of the precipitation in north China might alleviate the shortage of water there, but will not change the pattern of more rain in the south and less in the north; (3) in the southeastern basins, the precipitation of the multi-model ensemble (MME) and MIROC6 during 2011–2020 will be lower than that of 1961–2010 (− 6.53 to − 0.06%) under all SSPs scenarios. While the precipitation will increase obviously under all the SSPs scenarios, especially for the SSP5-8.5 scenario after the year of 2060; (4) the bias of the MME was much lower than that of individual CMIP6 models, and the bias of lower SSPs scenarios will be relatively lower. Generally, uncertainty ranges of precipitation fluctuations in north China (15.31–79.26%) will be higher than those in south China (16.06–7.55%). These findings revealed the projections and uncertainties of CMIP6 precipitation over China, which will be helpful for a better understanding of the future evolution of precipitation in China at large scale and in other regions of the world. [ABSTRACT FROM AUTHOR]

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South‐East China usually experiences mild winter temperatures but can also be impacted by large‐scale and long‐lasting cold events. Here, we investigate how this category of events has changed during the past 6 decades and why, using both observation and climate model ensembles. We first show that models can reproduce the observed largescale dynamics associated with cold events. A dynamical advection of cold air is mostly responsible not only to trigger cold surges but also we found strong feedback from shortwave radiation, decreasing due to enhanced cloud cover. Recent trends are more difficult to evaluate. Models show a large internal variability and intermodel spread, while observation also indicates large uncertainties in its trend due to high internal variability. Our analysis indicates that even if cold events are likely to have been reduced due to greenhouse gases, trends cannot be attributed with high confidence to any anthropogenic signal alone. However, in the next few decades, the frequency of long‐lasting cold events are expected to quickly reduce due to the emerging greenhouse gases signal and possibly weakening aerosol effect over South‐East China. We also found that most of the trends are due to the change in the mean temperature. Thus, indirectly, there is no clear evidence that the change in cold event frequency is due to a change in the circulation. Key Points: Change in the frequency of cold waves over South‐East China during the past decades is not detectable yetCMIP5 and CMIP6 models can reproduce the observed dynamics of these eventsCold waves are expected to decrease due to the dominant impact of greenhouse gases in the next decades [ABSTRACT FROM AUTHOR]

The characteristics of near-surface wind speed (NWS) are important to the study of dust storms, evapotranspiration, heavy rainfall, air pollution, and wind energy development. This study evaluated the performance of 30 models of the Coupled Model Intercomparison Project Phase 6 (CMIP6) through comparison with observational NWS data acquired in China during a historical period (1975–2014), and projected future changes in NWS under three scenarios (SSP1-2.6, SSP2-4.5, and SSP5-8.5) based on an optimal multi-model ensemble. Results showed that most models reproduced the spatial pattern of NWS for all seasons and the annual mean, although the models generally overestimated NWS magnitude. All models tended to underestimate the trends of decline of NWS for all seasons and the annual mean. On the basis of a comprehensive ranking index, the KIOST-ESM, CNRM-ESM2-1, HadGEM3-GC31-LL, CMCC-CM2-SR5, and KACE-1-0-G models were ranked as the five best-performing models. In the projections of future change, nationally averaged NWS for all months was weaker than in the historical period, and the trends decreased markedly under all the different scenarios except the winter time series under SSP2-4.5. Additionally, the projected NWS over most regions of China weakened in both the early period (2021–2060) and the later period (2061–2100). [ABSTRACT FROM AUTHOR]