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Climate change is the dominant factor affecting the hydrological process, it is of great significance to simulate and predict its influence on water resources management, socio-economic activities, and sustainable development in the future. In this paper, the Xiying River Basin was taken as the study area, China Atmospheric Assimilation Driven Data Set (CMADS) and observation data from the Jiutiaoling station were used to simulate runoff of the SWAT model and calibrate and verify model parameters. On this basis, runoff change of the basin under the future climate scenario of CMIP6 was predicted. Our research shows that: (1) The contribution rates of climate change and human activities to runoff increase of the Xiying River are 89.17% and 10.83%, respectively. Climate change is the most important factor affecting runoff change of the Xiying River. (2) In these three different emission scenarios of SSP1-2.6, SSP2-4.5 and SSP5-8.5 in CMIP6 climate model, the average temperature increased by 0.61, 1.09 and 1.74 °C, respectively, in the Xiying River Basin from 2017 to 2050. Average precipitation increased by 14.36, 66.88, and 142.73 mm, respectively, and runoff increased by 15, 24, and 35 million m³, respectively. The effect of climate change on runoff will continue to deepen in the future. [ABSTRACT FROM AUTHOR]

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]

The latest Coupled Model Intercomparison Project Phase 6 (CMIP6) proposes new shared pathways (SSPs) that incorporate socioeconomic development with more comprehensive and scientific experimental designs; however, few studies have been performed on the projection of future multibasin hydrological changes in China based on CMIP6 models. In this paper, we use the Equidistant Cumulative Distribution Function method (EDCDFm) to perform downscaling and bias correction in daily precipitation, daily maximum temperature, and daily minimum temperature for six CMIP6 models based on the historical gridded data from the high-resolution China Meteorological Forcing Dataset (CMFD). We use the bias-corrected precipitation, temperature, and daily mean wind speed to drive the variable infiltration capacity (VIC) hydrological model, and study the changes in multiyear average annual precipitation, annual evapotranspiration and total annual runoff depth relative to the historical baseline period (1985–2014) for the Chinese mainland, basins and grid scales in the 21st century future under the SSP2-4.5 and SSP5-8.5 scenarios. The study shows that the VIC model accurately simulates runoff in major Chinese basins; the model data accuracy improves substantially after downscaling bias correction; and the future multimodel-mean multiyear average annual precipitation, annual evapotranspiration, and total annual runoff depth for the Chinese mainland and each basin increase relative to the historical period in near future (2020–2049) and far future (2070–2099) under the SSP2-4.5 and SSP5-8.5 scenarios. The new CMIP6-based results of this paper can provide a strong reference for extreme event prevention, water resource utilization and management in China in the 21st century. [ABSTRACT FROM AUTHOR]

Changing climate has altered the trends and variability of precipitation and temperature globally and thereby increasing the risk of natural and social disasters, especially in coastal climatic transitional zones such as the Huaihe river basin (HRB). This paper applies the Empirical Quantile Mapping (EQM) method for bias correction and systematically evaluates the performance of the 30 Global climate models (GCMs) of the Coupled Model Intercomparison Project phase 6 (CMIP6) in simulating precipitation and temperature over the Huaihe river basin (HRB) for 1979–2014. A suitable multi-model ensemble subset (BMME) is selected based on the Euclidean Distance (ED) synthetic metric. Results show that in comparison to the baseline period 1995–2014, precipitation (temperature) over HRB is projected to increase at the rate of around 15 mm/decade (0.1 °C/decade), 16 mm/decade (0.3 °C/decade), 20 mm/decade (0.5 °C/decade), and 15 mm/decade (0.6 °C/decade) for the period 2015–2100, under Shared Socioeconomic Pathways SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5, respectively. In the long-term (2081–2100), the annual precipitation is projected to increase by 32%, 27%, 35%, and 26%, under the four scenarios, respectively. The temperature is projected to remain stable or slightly decrease under SSP1-2.6 (2 °C) and SSP2-4.5 (3 °C), while will keep rising and increasing by 6 °C under SSP5-8.5 and by 4 °C under SSP3-7.0 by 2100. The isotherm and isohyet are projected to shift northwest from southeastern coastal China to inland in the future, which is likely associated with the northwestward shift of the western Pacific anticyclone in summer. [ABSTRACT FROM AUTHOR]

Land use change is closely related to the terrestrial ecosystem. Studying the relationship between land use and ecosystem service carbon storage under future climate change can optimize the regional ecosystem service function and formulate sustainable social and economic development policies, which is of great significance for regional carbon emission management and climate change response. In this paper, the coupled model of SSP-RCP-PLUS-InVEST was constructed to simulate land use change under different scenarios, analyze the influence degree of driving factors on different regions, and explore the spatio-temporal evolution and spatial correlation of carbon storage, taking Central Yunnan City agglomeration as an example. The results showed that: (1) from the perspective of land use change, the overall changes in SSP126 and SSP245 scenarios were similar, and the change rates of different types were slightly different. Under the SSP585 scenario, the forest area decreased, and the urban land and cropland land expanded. (2) Under the three scenarios in the future, the decrease in total carbon storage is the most serious under the SSP245 scenario, and the decrease is the most serious under the SSP585 scenario. (3) Comparative spatial correlation: the carbon storage in the Central Yunnan urban agglomeration is generally correlated among counties, and there is no significant difference under the future scenarios, showing a high accumulation pattern in the east and low accumulation pattern in the middle. In general, reasonable planning of land spatial patterns and increasing the proportion of forest is conducive to the proportion of regional carbon sequestration capacity. The research methods can provide references for addressing climate change and territorial spatial planning. [ABSTRACT FROM AUTHOR]

Extreme temperature events are becoming more frequent due to global warming, and have critical effects on natural ecosystems, social and economic spheres, human production and life. Predicting changes in temperature extremes and trends under future climate scenarios helps to assess the impact of climate change accurately. Based on climate observations from 54 meteorological stations in the North China Plain and the projection data from seven general circulation models (GCMs) from the Coupled Model Intercomparison Project phase 6 (CMIP6), this paper researches nine representative extreme temperature indices under four typical climate scenarios. The aim is to reveal the temporal and spatial variations in extreme temperature indices in the North China Plain during the past (1971–2010) and the future (2061–2100). The results show that: using a support vector machine (SVM) to perform regression analysis on the multi-GCMs prediction results, the root mean square error (RMSE) and relative standard deviation (RSD) of the multi-model ensemble simulations obtained by the SVM method are lower than those of the arithmetic mean method and can better match the trend of the historical extreme temperature index; the extreme high temperature index is predicted to show a significant upward trend in the future, while the extreme low temperature index will decrease significantly; and there are significant spatial differences in the extreme temperature index in both historical and future periods, with the extreme temperature index under the high radiation forcing scenario (SSP585) showing the most considerable variation and the most significant spatial differences. [ABSTRACT FROM AUTHOR]

This paper looks at regional water security in eastern China in the context of global climate change. The response of runoff to climate change in the Qinhuai River Basin, a typical river in eastern China, was quantitatively investigated by using the Soil and Water Assessment Tool (SWAT) model and the ensemble projection of multiple general circulation models (GCMs) under three different shared socioeconomic pathways (SSPs) emission scenarios. The results show that the calibrated SWAT model is applicable to the Qinhuai River Basin and can accurately characterize the runoff process at daily and monthly scales with the Nash–Sutcliffe efficiency coefficients (NSE), correlation coefficients (R), and the Kling–Gupta efficiency (KGE) in calibration and validation periods being above 0.75 and relative errors (RE) are ±3.5%. In comparison to the baseline of 1980–2015, the mean annual precipitation in the future period (2025–2060) under the three emission scenarios of SSP1-2.6, SSP2-4.5, and SSP5-8.5 will probably increase by 5.64%, 2.60%, and 6.68% respectively. Correspondingly, the multiple-year average of daily maximum and minimum air temperatures are projected to rise by 1.6–2.1 °C and 1.4–2.0 °C, respectively, in 2025–2060. As a result of climate change, the average annual runoff will increase by 16.24%, 8.84%, and 17.96%, respectively, in the period of 2025–2060 under the three SSPs scenarios. The increase in runoff in the future will provide sufficient water supply to support socioeconomic development. However, increases in both rainfall and runoff also imply an increased risk of flooding due to climate change. Therefore, the impact of climate change on flooding in the Qinhuai River Basin should be fully considered in the planning of flood control and the basin's development. [ABSTRACT FROM AUTHOR]

The arid region of northwest China (ARNC) is one of the most sensitive areas to global warming. However, the performance of new Global Climate Models (GCMs) from phase 6 of the Coupled Model Intercomparison Project (CMIP6) in simulating climate in this region, especially in the subregions, is not clear yet. Based on the temperature dataset from historical runs of CMIP6, this paper analyzed and evaluated the simulation ability of 29 GCMs in reproducing the annual mean temperature (tas), annual mean maximum temperature (tasmax) and annual mean minimum temperature (tasmin) in the ARNC and subregions from 1961 to 2014. The results show that (1) the correlation coefficients (CCs) between simulation and observation time series for the mean of two model ensembles (MME for equal-weight multi-model ensemble and PME for preferred-model ensemble) are generally better than those of 29 individual GCMs, with CCs ranging from 0.38 to 0.87 (p < 0.01). (2) All the models can simulate the significant warming trend of the three temperature elements in the study area well. However, the warming magnitude simulated by most of the models (41%) is smaller than the observations except for tasmax, which is also shown in the MME. (3) The spatial pattern of the three temperature elements can be better reflected by most models. Model simulation ability for the ARNC is better compared to that of the four subregions, with a spatial CC greater than 0.7 (p < 0.01). Among the subregions, the simulation performance of the north of Xinjiang for spatial pattern is superior to that of the other regions. (4) The preferred models for each subregion are various and should be treated differently when used. Overall, the PME outperforms both the MME and the individual models; it can not only simulate the linear trend accurately but also reduce the deviation effectively. [ABSTRACT FROM AUTHOR]

The increased growth of vegetation has the potential to slow global climate warming. Therefore, analyzing and predicting the response assessment of Chinese vegetation to climate change is of great significance to studies of global warming. In this paper, we examine the spatiotemporal dynamics of vegetation leaf area index (LAI) values in China from 1981 to 2017 and their correlations with meteorological (hydrothermal) factors based on trend analysis and correlation analysis. We further construct an LAI prediction model based on hydrothermal conditions. The climate data obtained under different scenarios in the CMIP5 and CMIP6 climate models were used to predict the dynamic change trend of vegetation LAI from 2021 to 2100. The results show that most areas of China (72.82%) showed an improving trend in vegetation LAI from 1981 to 2017, during which the annual average LAI value increased at a rate of 0.0029 year−1. Vegetation LAI in China was significantly correlated with climatic factors (temperature, precipitation, and evapotranspiration), and the LAI prediction model constructed based on hydrothermal conditions had a high accuracy (Pearson's Cor value is 0.9729). From 2021 to 2100, approximately 2/3 of China's vegetation LAI area showed an improvement trend, and the impact of climate change on vegetation LAI predictions under the high emission scenario was greater than that under the low emission scenario. This research can provide a basis for studies on the climatic drivers of vegetation change and the global vegetation dynamic model. [ABSTRACT FROM AUTHOR]

Compound climate extremes, such as events with concurrent temperature and precipitation extremes, have significant impacts on the health of humans and ecosystems. This paper aims to analyze temporal and spatial characteristics of compound extremes of monthly temperature and precipitation, evaluate the performance of the sixth phase of the Coupled Model Intercomparison Project (CMIP6) models in simulating compound extremes, and investigate their future changes under Shared Socioeconomic Pathways (SSPs). The results show a significant increase in the frequency of compound warm extremes (warm/dry and warm/wet) but a decrease in compound cold extremes (cold/dry and cold/wet) during 1985–2014 relative to 1955–1984. The observed upward trends of compound warm extremes over China are much higher than those worldwide during the period of interest. A multi‐model ensemble (MME) of CMIP6 models performs well in simulating temporal changes of warm/wet extremes, and temporal correlation coefficients between MME and observations are above 0.86. Under future scenarios, CMIP6 simulations show substantial rises in compound warm extremes and declines in compound cold extremes. Globally, the average frequency of warm/wet extremes over a 30‐yr period is projected to increase for 2070–2099 relative to 1985–2014 by 18.53, 34.15, 48.79, and 59.60 under SSP1‐2.6, SSP2‐4.5, SSP3‐7.0, and SSP5‐8.5, respectively. Inter‐model uncertainties for the frequencies of compound warm extremes are considerably higher than those of compound cold extremes. The projected uncertainties in the global occurrences of warm/wet extremes are 3.82 times those of warm/dry extremes during 2070–2099 and especially high for the Amazon and the Tibetan Plateau. Plain Language Summary: Compound climate extremes, such as the events with concurrent temperature and precipitation extremes, have significant impacts on the health of humans and ecosystems. Can climate model simulate the historical compound extremes? If yes, how the global compound extremes will change in the future? In this study, we found that the global climate model performs well in simulating temporal changes of warm/wet and warm/dry extremes during the period 1955–2014. With greenhouse gas emissions continuing to increase in the future, compound warm/dry and warm/wet extremes show a continuous increase in frequency in the next few decades, while compound cold/dry and cold/wet extremes are projected to occur less frequently. Key Points: A multi‐model ensemble of CMIP6 models performs well in simulating temporal changes of warm/wet extremesThe inter‐model uncertainties for the frequencies of compound warm extremes are considerably higher than those of compound cold extremesThe projected uncertainties in the global occurrences of warm/wet extremes are 3.82 times those of warm/dry extremes during 2070–2099 [ABSTRACT FROM AUTHOR]

In the context of global climate change and ecological restoration projects, significant changes have been observed in the fractional vegetation cover (FVC) in the Yellow River basin. The increased vegetation growth accelerates water consumption, exacerbating drought and water scarcity issues, thereby heightening regional water resource shortage risks. This study targets the Yellow River basin in China, employing a pixel-based model to convert NDVI into FVC datasets. We establish a pixel-wise mathematical model for annual runoff and environmental factors based on residual analysis and methods like multiple linear regression. Using climate model data from CMIP6 as independent variables, in conjunction with the statistical model, we elucidate the spatiotemporal characteristics of annual runoff in the Yellow River basin under future climate scenarios. Our results indicate that, under four different climate scenarios, the average annual runoff in the Yellow River basin is projected to increase. The increases are quantified as 0.008 mm/a, 0.065 mm/a, 0.25 mm/a, and 0.24 mm/a for SSP126, SSP245, SSP370, and SSP585 scenarios, respectively. From 2022 to 2040, the spatial distribution of the runoff change rates under the SSP245 and SSP370 scenarios show an increasing trend in upstream areas such as the Qinhe and Longmen regions, with rates ranging from 6.00 to 8.61 mm/a. During the period from 2041 to 2060, all four climate scenarios indicate minimal changes in the runoff depth in the northern part of the Yellow River basin. From 2061 to 2080, under the SSP126 and SSP245 scenarios, the spatial distribution of the runoff shows significant increases in the river source area and a decreasing trend in the middle reaches, with rates ranging from 4.52 to 11.39 mm/a. For the period from 2081 to 2100, the runoff change rates vary significantly under the four climate scenarios. These findings provide a detailed understanding of how future climate scenarios could impact water resource distribution in the Yellow River basin, offering critical insights for regional water management and policy making to mitigate potential water scarcity challenges. [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]

Whether there is a transition underway, from a warm-dry climate to a warm-wet climate in Northwest China remains a controversial and scientifically significant issue. Will this trend continue in the future? Another hot issue is whether the climate in Northwest China will continue to be warm and humid over the next few decades. In this paper, eight CMIP6 models were employed to investigate temperature and precipitation changes under five principal Shared Socioeconomic Pathway (SSP) scenarios (from 2015 to 2099) to project the future warming and humidification in Northwest China using the SPEI (standardized precipitation evapotranspiration index) method. The results revealed that (1) the simulated temperature and precipitation of eight CMIP6 models were consistent with that of observed data during 1961-2014, which showed an increase of approximately 28.2 mm, while simulated data revealed an increase of approximately 9.4 mm. The annual precipitation gradually decreased from Eastern Inner Mongolia and the Southern Northwest Mongolia region (.700 mm) to the Central Northwest Mongolia region (,100 mm) from 1961 to 2014; (2) the MME significantly overestimated the temperature and slightly underestimated the precipitation in Northwest Mongolia. The temperature difference between the simulated and observed data was approximately 0.4 °C. The observed data showed an increase of approximately 0.9 °C from 1961 to 2014, whereas the simulated data revealed an increase of approximately 0.7 °C; (3) in the SSP5-8.5 scenario, the percentage of precipitation anomalies at 1.5, 2, 3, and 4 °C were 166.64, 190.58, 226.44, and 274.56%, respectively; thus, alleviating the drought situation while facilitating the warm-dry to warm-wet climate transition; (4) the water balance between rising temperatures and increased evapotranspiration resulting from increased precipitation suggested that not all sites will be wet in the future. There was still a drying trend in some areas, where drought was more severe under the high emissions scenario than the low emissions scenario. [ABSTRACT FROM AUTHOR]

While previous studies have largely focused on anthropogenic warming characterized by surface air temperature, little is known about the behaviors of human‐perceived temperature (HPT), which describe the "feels‐like" equivalent temperature by considering the joint effects of temperature, humidity and/or wind speed. Here we adopted an optimal fingerprinting method to compare seasonal mean HPTs in China with those from simulations conducted with multiple climate models participating in the Coupled Model Intercomparison Project Phase 6. We found clear anthropogenic signals in the observational records of changes in both summer and winter HPTs over the period 1971–2020. Moreover, the anthropogenic greenhouse gas influence was robustly detected, with clear separation from natural and anthropogenic aerosol forcings. The anthropogenic greenhouse gas forcing plays the dominant role (>90%) of human‐perceived warming. Urbanization effects contribute slightly and moderately to the estimated trends in summer and winter HPTs, respectively, in addition to the effects of external forcing. Plain Language Summary: Human influences have been identified in the observed warming quantified by surface air temperature (SAT), but SAT alone is inadequate as a metric for human thermal comfort. Here we focus on human‐perceived temperature (HPT), which describes the "feels‐like" equivalent temperature by considering the joint effects of temperature, humidity, and/or wind speed. We isolate anthropogenic impacts on the observed increase in summer and winter HPTs in China during 1971–2020 by comparing observations with state‐of‐the‐art climate models. Results show that the influence of anthropogenic greenhouse gas is detected, with clear separation from other external forcings such as solar and volcanic activities and anthropogenic aerosols. The human‐induced greenhouse gas increases are also found to explain most (>90%) of the observed human‐perceived warming. Along with the effects of large‐scale anthropogenic forcing, urbanization effects also have a slight to moderate influence on the estimated trends in summer and winter HPTs. Our work is an early attempt to provide quantitative evidence for the physiological impacts of anthropogenic global warming and local urbanization on human beings. Key Points: The warming is quantified by human‐perceived temperature that considers the joint effects of temperature, humidity and/or wind speedHuman influence could be robustly detected in both summer and winter human‐perceived warmingThe observed increase in human‐perceived temperature is mostly attributed to anthropogenic greenhouse gas increases [ABSTRACT FROM AUTHOR]

Recent studies on China's arid and semi-arid regions, particularly the Tarim River Basin (TRB), have shown an increase in the intensity and frequency of extreme weather events. This research examines the link between meteorological droughts, as measured by the Standardized Precipitation Evapotranspiration Index (SPEI), and hydrological droughts, as indicated by the Standardized Runoff Index (SRI) and the Standardized Terrestrial Water Storage Index (STI), over various time scales. Historical data indicate that SPEI drought frequency (DF) was 14.3–21.9%, with prevalent events in the northern oases. SRI DF ranged from 9.0% to 35.8%, concentrated around the Taklamakan and Kumtag Deserts, while STI DF varied between 4.4% and 32.7%, averaging 15% basin-wide. Future projections show an increased DF of SPEI in deserts and a decrease in oases; SRI DF decreased in deserts but increased in oases. STI changes were more moderate. The study also found a higher risk of drought progression from SPEI to SRI in the southwestern and northeastern oases, exceeding 50% probability, while central and eastern TRB had lower risks. The western TRB and inner Taklamakan Desert faced higher risks of SPEI to STI progression, with probabilities over 45%, in contrast to the lower risks in the eastern and central oases. The concurrence of SRI/STI with moderate to extreme SPEI droughts led to a higher probability and area of SRI/STI droughts, whereas consistent SPEI types showed a reduced induced probability and extent of SRI/STI droughts. This study enhances the understanding of drought propagation from meteorological to hydrological droughts in the TRB and contributes to the prevention of hydrological drought to a certain extent. [ABSTRACT FROM AUTHOR]

The synchronicity of rain and heat in the summer of China's monsoon region provides sufficient water and heat resources for maize growth. However, the intra-annual distribution of precipitation and the probability of extreme precipitation have been inevitably altered by the ongoing climate change, thus affecting the matching degree between precipitation and crop water requirements (MDPCWR). Evaluating the extent to which the MDPCWR will change in the future is of great importance for food security and the sustainable management of water resources. In this study, considering that different growth stages of crops have different sensitivities to water stress, the AquaCrop model was used to calculate the MDPCWR more accurately. In addition, a cumulative distribution function-transform (CDF-t) method was used to remove the bias of 11 global climate models (GCMs) under two typical emission scenarios (SSP2-4.5 and SSP5-8.5) from phase six of the Coupled Model Intercomparison Project (CMIP6). A comprehensive investigation was conducted on how maize growth, water consumption, and the MDPCWR will respond to future climate change with CO2 concentration enrichment in the Huang–Huai–Hai (3H) region in China by driving a well-tested AquaCrop model with the bias-corrected GCMs outputs. The results indicate the following: (1) The CDF-t method can effectively remove seasonal bias, and it also performs well in eliminating the bias of extreme climate events. (2) Under the SSP2-4.5 scenario, the average maximum temperature will increase by 1.31 °C and 2.44 °C in 2021–2050 and 2051–2080, respectively. The average annual precipitation will increase up to 96.8 mm/year, but it will mainly occur in the form of heavy rain. (3) The increased maize evapotranspiration rate does not compensate for the decreased crop water requirement (up to −32 mm/year), due to a shorter growth cycle. (4) The farmland cultivation layer is not able to hold a significant amount of precipitation, due to the increased frequency of heavy rains, resulting in increased irrigation water requirements for maize over the next two periods, with the maximum value of 12 mm/year. (5) Under different scenarios, the projected future MDPCWR will decrease by 9.3–11.6% due to changes in precipitation patterns and crop water requirements, indicating that it will be more difficult for precipitation to meet the water demand of maize growing in the 3H region. The results can provide comprehensive information to understand the impact of climate change on the agricultural water balance and improve the regional strategy for water resource utilization in the 3H region. [ABSTRACT FROM AUTHOR]

Reference evapotranspiration (ET0), an essential variable used to estimate crop evapotranspiration, is expected to change significantly under climate change. Detecting and attributing the change trend in ET0 to underlying drivers is therefore important to the adoption of agricultural water management under climate change. In this study, we focus on a typical agricultural region of the Fenwei Plain in northern China and use the Mann–Kendall test and contribution rate to detect the change and trend in ET0 at annual and seasonal scales and determine the major contribution factors to ET0 change for the baseline period (1985–2015) and the future period (2030–2060) based on high-resolution gridded data and climatic data from the Coupled Model Intercomparison Project Phase 6 (CMIP6). The results indicate that the annual ET0 of the Fenwei Plain showed a significant decreasing trend in the baseline period but insignificant and significant increasing trends in the future period under the SSP245 and SSP585 scenarios, respectively. The annual ET0 of the plain under the SSP245 and SSP585 scenarios increase by 4.6% and 3.0%, respectively, compared to the baseline period. The change and trend in ET0 between the four seasons are different in the baseline and future periods. Winter and autumn show clear increases in ET0. VPD is the major contribution factor to the ET0 change in the plain. The change in ET0 is mainly driven by the climatic variables that change the most rather than by the climatic variables that are the most sensitive to the ET0 change. The change and trend in ET0 in the plain showed clear spatial differences, especially between the eastern and western area of the plain. To adapt to the impact of climate change on ET0, the irrigation schedule of the crops cultivated in the plain, the cropping system and management of the irrigation district in the plain need to be adjusted according to the change characteristics of spatial and temporal ET0 in the future. These results contribute to understanding the impacts of climate change on evapotranspiration in the study region and provide spatial and temporal references for adaptation in managing agricultural water use and crop cultivation under climate change. [ABSTRACT FROM AUTHOR]

This study develops a four-module-based multimodel framework, coupled with the projections of general circulation models (GCMs), a hydrological model (SWAT), and two sets of evaluation indicators [Indicators of Hydrologic Alteration (IHA) and River Regime Index (RRI)], to systematically quantify the response of ecohydrological regimes to climatic change. The Jinsha River Basin (JRB) was selected as a target area for both framework verification and application analysis because of its precious natural conditions, privileged abundant hydropower, and extremely rich fish resources. Results revealed that: (1) temperature and precipitation in the JRB would show increasing trends to varying degrees in the future. The increase in temperature would be much higher than that in precipitation, and the increment would be more significant during the end of the century (2068–2097). (2) The future runoff in the JRB may face an overall decreasing trend, leading to more and more frequent drought disasters. (3) The disturbances in the ecohydrological regime would result in more concentrated runoff and smoother hydrological pulse fluctuations in the JRB, meaning human-assisted participation would be required for the conservation of reproduction and development of ecological resources like Coreius guichenoti. Our findings suggest that effective management of future watershed resources can only be accomplished if an in-depth and comprehensive hydrological evaluation of changing climate is made, and the protection and sustainable development of ecological resources of the JRB in the future will require the participation of stakeholders. Figuring out the alteration in ecohydrological regimes under natural variation and its potential hazards may guide the degree of human participation in futural river protection. [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]

Our previous study revealed the link between Barents–Kara sea ice and rainfall in eastern China. This study continues evaluating the performance of multiple models from phase 6 of the Coupled Model Intercomparison Project (CMIP6) in simulating this linkage. Most CMIP6 models can simulate Arctic sea ice coverage in the present climate system, although the sea ice extent in the edge areas show some biases. Only a few models can roughly reproduce the observed rainfall dipole pattern associated with Arctic sea ice variability. The linkage between Arctic sea ice variability in winter and eastern China rainfall in early summer is performed through a long memory of the sea ice, the stratospheric variability as the mediator, and downward propagation of stratospheric signals. Very few CMIP6 models can exhibit a realistic interannual relationship between the Arctic sea ice and China rainfall. The selected high-skill models with a more realistic linkage between sea ice and China rainfall present a clear downward impact of the stratospheric circulation anomalies associated with sea ice variability. The reversal of the Northern Hemisphere Annular Mode (NAM) from the negative phase in early winter to the positive phase in spring in the high-skill models and observations denotes the important role of the stratosphere as a mediator to bridge the Arctic sea ice and China rainfall. The long memory of the Arctic sea ice with the stratosphere as the mediator has a deep implication on the seasonal forecasts of East Asian countries. [ABSTRACT FROM AUTHOR]

Climatic variability and the quantification of climate change impacts on hydrological parameters are persistently uncertain. Remote sensing aids valuable information to streamflow estimations and hydrological parameter projections. However, few studies have been implemented using remote sensing and CMIP6 data embedded with hydrological modeling. This research studied how changing climate influences the hydro-climatic parameters based on the earth system models that participated in the sixth phase of the Coupled Model Intercomparison Project (CMIP6). GRACE evapotranspiration data were forced into the Soil and Water Assessment Tool (SWAT) to project hydrologic responses to future climatic conditions in the Hongshui River basin (HRB) model. A novel approach based on climate elasticity was utilized to determine the extent to which climate variability affects stream flow. CMIP6 SSPs (shared socio-economic pathways) for the second half of the 20th century (1960–2020) and 21st century (2021–2100) projected precipitation (5–16%) for the whole Hongshui River basin (HRB). The ensemble of GCMs projected an increase of 2 °C in mean temperature. The stream flow is projected to increase by 4.2% under SSP-1.26, 6.2% under SSP-2.45, 8.45% under SSP-3.70, and 9.5% under SSP-5.85, based on the average changes throughout the various long-term future scenarios. We used the climate elasticity method and found that climate change contributes 11% to streamflow variability in the Hongshui River basin (HRB). Despite the uncertainty in projected hydrological variables, most members of the modeling ensemble present encouraging findings for future methods of water resource management. [ABSTRACT FROM AUTHOR]

The Fifth IPCC Assessment Report indicates that climate change will affect crop growth and threaten the stability of food systems. Accumulated temperature, which is closely related to vegetation phenology and cropping systems, is an important indicator of heat in a region. Studying the history and future accumulated temperature changes can provide scientific reference for the change of crop phenology and cropping system, which is important for the improvement of grain production in China. Based on the MK trend test, MK abrupt change test and interpretable machine learning model, this study analyzes the spatial and temporal variation of accumulated temperature in China from 1979 to 2018, predicts its future variation based on CMIP6, and investigates the dominant influencing factors among different agricultural regions. The study found that (1) the accumulated temperature belt shows a northward shift and retreat trend toward higher altitudes, and the area of the high accumulated temperature belt increases year by year, leading to the narrowing of the area of the low accumulated temperature belt year by year, and the trend remains unchanged under the future scenario; meanwhile, the northward shift trend of the accumulated temperature belt is greatly mitigated and curbed under the SSP126 scenario. (2) The changes of accumulated temperature belt are mainly influenced by the increase of accumulated temperature duration days, and secondarily by the increase of temperature. The contribution brought by the first day of accumulated temperature from 1979 to 2018 is greater than that brought by the last day of accumulated temperature, while in the future scenario, on the contrary, changes in vegetation phenology delay should be given more attention. [ABSTRACT FROM AUTHOR]

This study investigates the performance of the 23 CMIP6 models in simulating spring precipitation over China and preliminarily explores the reasons for the underestimation of spring precipitation over South China (SC). The results show that the CMIP6 models can generally capture spring precipitation over China but have distinct underestimations in SC. In addition to analysing the results of the multimodel ensemble mean (MME), based on the regional mean spring precipitation differences over SC, we also analyse five models with greater overestimated precipitation amounts composing the high‐value MME (H_MME) and five models with greater underestimated precipitation amounts composing the low‐value MME (L_MME) as comparisons. Moisture budget analysis reveals that the underestimated dynamic component of vertical moisture advection mainly causes the negative biases of spring precipitation over SC. Further analyses indicate that the underestimation of the intensity of the East Asian subtropical jet (EASJ) generally causes weakened secondary meridional circulation to the south of 30°N and moisture convergence over SC in CMIP6 models. Consequently, less moistened air would be lifted to high levels, providing an unfavourable background for the occurrence of spring precipitation and inducing underestimation compared with the observations. The modelling EASJ shows much stronger (weaker) H_MME (L_MME), which generally enhances (weakens) the dynamic component of the secondary circulation over SC, favourable for the increase (decrease) in spring precipitation. [ABSTRACT FROM AUTHOR]

Sea surface salinity (SSS) is a crucial indicator that is used to monitor the hydrological cycle in the ocean system. In this study, we evaluated the simulation skill of the Coupled Model Intercomparison Project Phase 6 (CMIP6) models in reproducing the SSS in the Asian Marginal Seas (AMSs). The results show that the AMSs' SSS simulated by most CMIP6 models is generally in good agreement with the observations in terms of spatial patterns and seasonal variability. However, these models tend to overestimate the SSS in the Eastern Arabian Sea and the Bay of Bengal by up to 1.3 psu, while they underestimate the SSS in the Bohai Sea, the Yellow Sea, the Southern South China Sea, and the Indonesian Seas, with the bias exceeding −1.5 psu. Additionally, the seasonal variations in the Sea of Okhotsk, the Bay of Bengal, and the Arabian Sea exhibit large biases with phase shift or reversal in some CMIP6 models. Notably, the observed magnitudes in the AMSs are significantly higher than the global average of 0.2 psu, ranging from 0.22 to 1.19 psu. Furthermore, we calculated the projected trends in sea surface salinity under different future scenarios by using the CMIP6 models. The results reveal relatively larger SSS freshening trends in the second half of the 21st century compared to the first half. Specifically, the freshening trends for the Shared Socio-Economic Pathway (SSP) of low- (global radiative forcing of 2.6 W/m2 by the year 2100), medium- (global radiative forcing of 4.5 W/m2 by 2100), and high-end (8.5 W/m2 by 2100) pathways are 0.05–0.21, 0.12–0.39, and 0.28–0.78 psu/century, respectively. The most rapid freshening trends of SSS are observed in the East China Seas and the Indonesian Seas, which are over two times greater than the global mean. On the other hand, the SSS freshening trends in the Arabian Sea are slightly lower than the global mean SSS freshening trend. [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]

Planting suitability determines the distribution and yield of crops in a given region which can be greatly affected by climate change. In recent years, many studies have shown that carbon dioxide fertilization effects increase the productivity of temperate deciduous fruit trees under a changing climate, but the potential risks to fruit tree planting caused by a reduction in suitable planting areas are rarely reported. In this study, Maxent was first used to investigate the spatial distribution of five Pyrus species in China, and the consistency between the actual production area and the modeled climatically suitable area under the current climatic conditions were determined. In addition, based on Coupled Model Intercomparison Project Phase 6, three climate models were used to simulate the change in suitable area and the migration trend for different species under different emission scenarios (SSP1-2.6, SSP2-4.5, SSP3-7.0 and SSP5-8.5). The results showed that the suitable area for pear was highly consistent with the actual main production area under current climate conditions. The potential planting areas of P. ussuriensis showed a downward trend under all emission paths from 2020 to 2100; other species showed a trend of increasing first and then decreasing or slowing down and this growth effect was the most obvious in 2020–2040. Except for P. pashia, other species showed a migration trend toward a high latitude, and the trend was more prominent under the high emission path. Our results emphasize the response difference between species to climate change, and the method of consistency analysis between suitable planting area and actual production regions cannot only evaluate the potential planting risk but also provide a reasonable idea for the accuracy test of the modeled results. This work has certain guiding and reference significance for the protection of pear germplasm resources and the prediction of yield. [ABSTRACT FROM AUTHOR]

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The Tarim River, the largest inland river in China, sits in the Tarim River Basin (TRB), which is an arid area with the ecosystem primarily sustained by water from melting snow and glaciers in the headstream area. To evaluate the pressures of natural disasters in this climate‐change‐sensitive basin, this study projected flash droughts in the headstream area of the TRB. We used the variable infiltration capacity (VIC) model to describe the hydrological processes of the study area, Markov chain Monte Carlo to quantify the parameter uncertainty of the VIC model. Ten downscaled general circulation models from the Coupled Model Intercomparison Project Phase 6 (CMIP6) were used to drive the VIC model, and the standardized evaporative stress ratio was applied to identify flash droughts. The results demonstrated that the VIC model after Bayesian parameters uncertainty analysis can efficiently describe the hydrological processes of the study area. In the future (2021–2100), compared with the plain region, the alpine region has higher flash drought frequency and intensity. Compared with the historical period (1961–2014), the frequency, duration, and intensity of flash droughts tend to increase throughout the study area, especially for the alpine area. Moreover, based on variance decomposition, CMIP6 model is the most important uncertainty source for flash drought projection, followed by the shared socioeconomic pathway of climate change scenario and VIC model parameters. Plain Language Summary: The Tarim River is the largest inland river in China, which is an arid area with the ecosystem primarily sustained by water from melting snow and glaciers in the headstream area. Compared with long‐duration drought, flash drought is more difficult to forecast and identify. We used the variable infiltration capacity model to build a hydrological model of the headstream area of the Tarim River Basin, and the model output (ET data) was used to identify flash droughts. Based on 10 representative Coupled Model Intercomparison Project Phase 6 (CMIP6) models, this study projected the future flash droughts in the study area under climate change. The findings indicated that the alpine region is likely to experience a greater frequency and intensity of flash droughts in the future (2021–2100) compared to the plain region. Compared with the historical period (1961–2014), the frequency, duration, and intensity of flash droughts tend to increase throughout the study area, especially for the alpine area. In addition, the result of uncertainty decomposition revealed that the CMIP6 model is the most important uncertainty source for flash drought projection. Key Points: The hydrological processes of study area are well described by the variable infiltration capacity model and Bayesian uncertainty analysisCompared with the plain region, the alpine region has higher flash drought frequency and intensity in the futureThe Coupled Model Intercomparison Project Phase 6 model is the most important uncertainty source for flash drought projection [ABSTRACT FROM AUTHOR]

Historical changes and possible future projections of temperature extremes in China, in terms of return values of annual extreme temperatures, are examined based on daily maximum and minimum temperatures from station observations and multiple models of the fifth and sixth phases of the Coupled Model Intercomparison Project (CMIP). The observations suggest that increases in temperature extremes are largely attributable to the changing mean climate, while the varying natural variability also has an important impact, which depends on the index of the variability. The models simulate warm extremes reasonably well but underestimate the spatial heterogeneity and temporal trend of cold extremes in China. In comparison, Coupled Model Intercomparison Project Phase 6 (CMIP6) models have higher skill in simulating temperature extremes in China, showing smaller biases and intermodel variability. MRI‐ESM2‐0 and NorESM2‐LM from CMIP6 and GFDL‐ESM2M and NorESM1‐M from CMIP5 are selected as reference models based on the better performance in reproducing observed temperature extremes in China. In the future, projections from CMIP6 multimodel ensemble (MME, represented as the multimodel median) and reference models all show a continued uptick in temperature extremes, with statistically significant increases in warm extremes mainly in the north and increases in cold extremes prominent in most parts of China. Different individual models, which have similar historical simulations, yield divergent future trends of temperature extremes, which may be associated with different climate sensitivities of models. In addition, MME usage should be treated with caution since its smoothing on spatial heterogeneity and possible information from poor models. Plain Language Summary: Given the serious hazards of extreme temperature events, understanding changes in extreme temperatures and effectively predicting their future changes are critical to climate mitigation and adaptation. According to station observations and model data from the Coupled Model Intercomparison Project Phase 5 (CMIP5) and 6 (CMIP6), we find besides the varying temperature mean, the varying temperature variance also has an important impact on changes in extreme temperatures, and CMIP6 models have overall better performance than CMIP5 models in reproducing changes in extreme temperatures in China. Our results also emphasize that good performances in historical simulation cannot guarantee better projections in the future and projections of extreme temperatures from the multimodel ensemble sacrifice the spatial heterogeneity relative to individual reference models. Key Points: The impact of the varying natural variability on changes in temperature extremes depends on the variability indexCMIP6 models perform better than CMIP5 in simulating temperature extremes changes in ChinaMultimodel ensemble projections of extreme temperatures sacrifice spatial heterogeneity [ABSTRACT FROM AUTHOR]

This study evaluates the ability of 23 climate models from phase 6 of the Coupled Model Intercomparison Project (CMIP6) in simulating extreme climate events over China. The multimodel ensemble (MME) performs better than most individual models in reproducing the climatological mean distribution of all extreme indices. The MME can reproduce well the climatological mean distributions of five extreme climate indices over China, including annual total precipitation (PTOT), maximum consecutive 5‐day precipitation (RX5), simple daily intensity (SDII), maximum daily maximum temperature (TXX), and minimum daily minimum temperature (TNN), with Taylor skill scores exceeding 0.7. SDII and TXX are the most skilful precipitation and temperature extreme indices simulated by the MME, respectively. The MME has relatively lower skill in simulating the climatological mean distribution of warm days (TX90P) and cold nights (TN10P) over China. Future projections of these extreme climate indices by the end of the 21st century are explored with the MME under the SSP1‐2.6, SSP2‐4.5, and SSP5‐8.5 scenarios. The PTOT and RX5 in northwestern China are all projected to increase by more than 30% under SSP5‐8.5. R20 is projected to increase by 4–5 days over southeastern China under SSP5‐8.5. There are fewer (more) consecutive dry days over north China (south China), with a change of 5 days under SSP5‐8.5. The extreme temperature indices, including TX90P, TXX, and TNN, all increase with time and higher SSP scenarios. The three indices increase by 40–55%, 4–6°C, and 4–7°C under SSP5‐8.5 over east China, respectively. The TN10P decreases by more than 6% over east China. The changes in these extreme indices under SSP1‐2.6 and SSP2‐4.5 are similar to those under SSP5‐8.5 but with a smaller magnitude. Large uncertainties still exist in the future projections, especially under the high SSP scenarios. [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]

Global climate change is projected to increase the probability of occurrence and severity of droughts. Increased CO2 concentration drives partial closure of plant stomata and reduces evapotranspiration. However, the impact of reduced evapotranspiration due to CO2 on future droughts characteristics in China is unclear. In this study, we have used the Coupled Model Intercomparison Project Phase 6 (CMIP6) global climate model simulations (GCMs) under historical period, and four shared socioeconomic pathway scenarios (SSP1‐2.6, SSP2‐4.5, SSP3‐7.0, and SSP5‐8.5) to estimate drought. We used a widely applicable drought index (i.e., the self‐calibrating Palmer Drought Severity Index [scPDSI]) to evaluate the historical and future drought conditions, using the Penman–Monteith formula with and without the CO2 concentrations. The results show that the increasing trend of scPDSI slows down when the CO2 effect is considered in all scenarios, especially in Heilongjiang, northern Xinjiang, and the Qinghai–Tibet Plateau. The divergence in the slowdown trend among the different scenarios becomes greater after 2030, with higher emission scenarios implying a greater slowdown in the increasing trend of scPDSI. After considering CO2, drought characteristics such as the drought frequency, intensity, and drought area decrease. Increased CO2 concentration on vegetation physiological processes could mitigate future drought. Therefore, the effects of CO2 on plant physiology should be considered in studies of future drought trends to develop more realistic response and adaptation policies to future drought changes. [ABSTRACT FROM AUTHOR]

Periods of dimming and brightening have been recorded in observational datasets of surface solar radiation (SSR) between the mid‐20th century and present day. Atmospheric components affect SSR, including aerosols and clouds, though studies disagree somewhat about the relative effect of each component in different regions. Current Earth system models (ESMs) are unable to simulate observed trends in SSR. This study includes an investigation into observed SSR variations between 1961 and 2014 and an evaluation of the effects of cloud cover variations and impacts of aerosol extinction, using timeseries of SSR and cloud cover from in‐situ measurements. Historical simulations by 42 ESMs participating in the Coupled Model Intercomparison Project phase 6 (CMIP6) have also been studied and compared to observations. The observational study indicates that cloud cover has had a dampening effect on the variations of SSR and that emissions of aerosol and aerosol precursors are the main cause of the general trends in observed SSR in four regions—China, Japan, Europe, and the United States—during 1961–2014. The study of simulated SSR in CMIP6 yields the conclusion that current ESMs remain unable to simulate the magnitude of observed dimming and brightening in China, Japan, and the United States, but that the European SSR trends between 1961 and 2014 are fairly well reproduced in the ESMs. A rough quantification of the regional surface radiation extinction efficiency of aerosol and precursor emissions in the simulations is found to agree with observed values in Europe, but not in the other three regions. Plain Language Summary: Measurements of incoming sunlight at the Earth's surface between the 1960s and the 1980s show a substantial downward trend globally, and between the 1980s and the 2000s an upward trend has followed, albeit weaker. These two trends are often called dimming and brightening, respectively. The reasons for dimming and brightening are not clear, although evidence supports the role of atmospheric components, prominently aerosol emissions and clouds; changes in aerosol emissions and clouds can cause changes in incoming sunlight at the surface because of their ability to absorb or reflect sunlight on its way through the atmosphere. In this study, measurements of incoming sunlight at the surface are investigated and compared with simulations from state‐of‐the‐art Earth system models (ESMs). The study reinforce evidence that aerosol emission changes have been causal to observed regional dimming and brightening, and suggests that cloud cover changes have not caused the trends. The study also finds that ESMs are unable to compute dimming and brightening trends in several regions, which may suggest that the models are not accurately reproducing the atmospheric processes that cause the trends. Key Points: This study supports evidence that dimming and brightening trends between 1961 and 2014 in China, Japan, Europe and the United States were caused by variations in aerosol‐ and aerosol precursor emissions, and not by cloud cover changesResults confirm and reinforce previous indications that Coupled Model Intercomparison Project phase 6 simulations are able to reproduce surface solar radiation trends observed in Europe, but not in China, Japan, and the United States [ABSTRACT FROM AUTHOR]

Reference evapotranspiration (ET0) is an integral part of the regional hydrological cycle and energy balance and is extremely sensitive to climate change. Based on temperature data from 24 global climate models (GCMs) in the Coupled Model Intercomparison Project Phase 6 (CMIP6), this study developed a multi-model ensemble based on delta statistical downscaling with multiple interpolation methods and evaluation indicators to predict the spatial and temporal evolution trends of ET0 in the Yellow River Basin (YRB) under four emission scenarios (SSP126, SSP245, SSP370, and SSP585) for the near- (2022–2040), mid- (2041–2060), and long- (2081–2100) term future. Results demonstrate that regional data generated based on delta statistical downscaling had good simulation performance for the monthly mean, maximum, and minimum temperatures in the YRB, and the developed multi-model ensemble had better simulation capability than any single model. Compared to the historical period (1901–2014), the annual ET0 showed a highly significant increase for different future emission scenarios, and the increase is faster with increasing radiative forcing. The first main cycle of ET0 change was 52, 53, 60, and 48 years for the SSP126, SSP245, SSP370, and SSP585, respectively. ET0 in the YRB had positive values for EOF1 under all four emission scenarios, responding to a spatially consistent trend across the region. Compared to the historical period, the spatial distribution of ET0 under different future emission scenarios was characterized by being larger in the west and smaller in the east. As the radiative forcing scenario increased and time extended, ET0 significantly increased, with a maximum variation of 112.91% occurring in the western part of the YRB in the long-term future under the SSP585 scenario. This study can provide insight into the water cycle patterns of watersheds and scientific decision support for relevant departments to address the challenges of climate change. [ABSTRACT FROM AUTHOR]

Precipitation change is determined by changes in frequency and intensity. Based on hourly and daily precipitation data sets in China (1961–2014) and CMIP6 model, this study found that observed inconsistency in the frequency trend at the two time resolutions (hourly and daily) reflected precipitation becoming more concentrated at 13.4% of stations, resulting in the specific precipitation phenomenon of "more hours in one day, but fewer days." However, models exhibited opposite precipitation phenomenon at 16.5% of stations and cannot reproduce the widespread increase in the proportion of extreme precipitation amounts (PA) in the total PA, especially at an hourly resolution. Although stations with significant trend from CMIP6 approximately twice the observation, both CMIP6 and observations showed that frequency and intensity changes dominated the total PA trend at hourly and daily time resolutions, respectively. These results demonstrated that daily precipitation observations could not fully capture the important features of short‐duration rainfall. Plain Language Summary: Do the hourly precipitation frequency and intensity have trends consistent with those of daily precipitation? Based on hourly and daily rain gauge data sets in China (1961–2014) during May–September (warm season), we found that the daily precipitation frequency widely decreased over past decades, but the hourly precipitation frequency did not decrease significantly in most regions. Such a difference led to the phenomenon of "more hours in 1 day, but fewer days" of warm season precipitation. The contribution calculation further revealed that precipitation observed at a coarse time resolution (e.g., daily time scale) could not sufficiently capture the important features of short‐duration rainfall. Compared with the observations, however, the CMIP6 models simulated a significant decreasing trend in hourly precipitation frequency at many sites and thus exhibited the opposite precipitation phenomenon. Moreover, models cannot reproduce the widespread increase in the proportion of extreme precipitation amount (PA) in total PA, especially at an hourly resolution. However, the models exhibited good consistency with the observations in terms of the dominant factors of the PA trend or long‐term change, although the percentages of stations were obviously biased by the models, especially for the trend contribution. Key Points: Observed inconsistent frequency trend between daily and hourly precipitation causes "more hours in 1 day, but fewer days" rainfall featureModels show opposite rainfall feature and miss broad increase of extreme precipitation amount (PA) rate in total PA, especially hourly scaleBoth CMIP6 and observations show frequency and intensity changes dominated the total PA trend at hourly and daily time scales, respectively [ABSTRACT FROM AUTHOR]

The South Asian high (SAH) experienced a decadal weakening in the late 1970s under global warming. Based on an evaluation of the historical runs from CMIP6 models, we quantitatively assessed the contributions of different external forcing using "good" models that reasonably simulated the decadal decline of the SAH. All‐forcing runs yielded the weakened SAH after the late 1970s, albeit the decadal decline was underestimated by most models. Compared to the insignificant contributions of greenhouse gas and natural forcing, anthropogenic aerosol played a dominant role in the decadal decline of the SAH. The increased aerosol likely drove a cooling surface over the Tibetan Plateau (TP) and East China via its effect on radiation. Consequently, the weakened heat source over the TP and associated thermodynamic effects over East China would have driven a cooling of eddy temperature and cyclonic anomalies in the upper troposphere, respectively, thereby causing the decline of the SAH. Plain Language Summary: Under global warming, the South Asian high (SAH) measured by eddy geopotential height experienced a decadal decline in the late 1970s. However, the mechanism responsible for the decadal decline of the SAH has not been investigated. In this study, the contributions of external forcing to the decadal decline of the SAH are accessed using "good" CMIP6 models that can reasonably simulate the decadal changes of SAH. Results indicate that anthropogenic aerosol plays an important role in weakening the SAH. The increased aerosol emission over the Tibetan Plateau (TP) and East China cools the ground through the radiation effect. The weakened heat effect of TP and East China can contribute to the weakening of SAH. By contrast, the contributions of greenhouse gas and natural forcing are almost completely insignificant due to their homogenized and only very slight effects on the upper‐tropospheric eddy temperature over the SAH region, respectively. Key Points: ALL forcing can yield a weakening of the South Asian high (SAH), and anthropogenic aerosol provided the dominant contributionAnthropogenic aerosol drove the decline of the SAH by weakening the effect of the heat source over the Tibetan PlateauThe contribution of greenhouse gas and natural external forcing to the decadal decline of the SAH is insignificant [ABSTRACT FROM AUTHOR]

The May and June precipitation (known as early summer precipitation; ESP) is an important water resource for South China and Taiwan (SCTW). This study explores the similarities and differences between the Community Earth System Model Version 2‐Large Ensemble (CESM2‐LE) and the Coupled Model Intercomparison Project Phase 6 (CMIP6) multi‐model ensemble in simulating the characteristics of ESP over SCTW in the present day, and in projecting the related changes at the end of 21st century. For the present‐day simulation, our results showed that CESM2‐LE and CMIP6 outperformed each other for different examined features. CESM2‐LE was slightly better than CMIP6 in capturing the magnitude of ESP over SCTW, while CMIP6 was more capable of representing the interdecadal shifts in the occurrence timing of the ESP maximum. Both the CESM2‐LE and CMIP6 projections indicated that ESP will be enhanced and the phase of maximum ESP will be delayed, from peaking around mid‐June in the present to late June in the future. These changes can be attributed to an enhanced short‐wave trough over southwest China and a late intensification of the southwesterly wind ahead of the short‐wave trough, which help transport more moisture from the north of the South China Sea to SCTW, particularly in mid‐to‐late June. In addition, relative to CMIP6, CESM2‐LE showed less uncertainty in the projected increase in ESP and phase delay. This finding highlights that model diversity may play a more important role than internal variability in attributing the uncertainty of projected changes in ESP over SCTW. Key Points: Early summer rainfall over South China and Taiwan is projected to be enhanced, with a delayed peak occurred in late June in the futureInternal variability is less important than model diversity in changing the sign (increase or decrease) of projected early summer rainfallRoles of atmospheric circulation modulations on the projected changes in early summer rainfall are identified [ABSTRACT FROM AUTHOR]

Under global warming, according to results obtained from offline drought indices driven by projections of general circulation models (GCMs), future droughts in China will worsen but the results are not consistent. We analyzed changes in droughts covering the entire hydrologic cycle using outputs of GCMs of the 6th Coupled Model Intercomparison Project (CMIP6) for SSP2-4.5 and SSP5-8.5 climate scenarios, and compared the results with that of popular, offline drought indices [the self-calibrating Palmer Drought Severity Index (scPDSI), Standardized Precipitation Evapotranspiration Index (SPEI) and Standardized Precipitation Actual Evapotranspiration Index (SPAEI)]. Among meteorological, agricultural, and hydrological drought indices tested under both SSP scenarios, the results obtained from SPAEI and scPDSI agree better with univariate drought indices than SPEI. scPDSI generally agrees well with agricultural droughts (Standardized Soil Moisture Index with the surface soil moisture content; SSIS). Future droughts estimated using soil moisture analysis are more widespread than that from precipitation and runoff analysis in humid regions of South China by the end of the twenty-first century. In arid northwestern China and Inner Mongolia, drought areas and severity based on scPDSI and SSIS forced with the SSP scenarios show obvious decreasing trends, in contrast to increasing trends projected in South China. Trends projected using SPEI contradict those projected by other drought indices in non-humid regions. Therefore, selecting appropriate drought indices is crucial in project representative future droughts and provides meaningful information needed to achieve effective regional drought mitigation strategies under climate warming impact. [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]

Extreme climate events undoubtedly have essential impacts on terrestrial ecosystems, but the spatiotemporal patterns of extreme climate events at regional scales are unclear. In this study, based on observations and 14 CMIP6 global climate models, we analyzed the spatiotemporal changes in extreme temperature events at the mainland China scale and different basin scales in historical and future periods, and their relative importance for the changes in mean temperature (Tmean). The results show that at the mainland China scale in the historical period, extreme cold days and extreme cold nights significantly decreased, while Tmean, extreme warm days, and extreme warm nights significantly increased. However, the rates of increase in Tmean and extreme temperature events in the Continental Basin, Southwest Basin and Yellow River Basin are higher than that at the mainland China scale. The multi-model ensemble is the best model for simulating extreme temperature events in mainland China. At the mainland China scale in the future, the trends of Tmean and extreme temperature events are slow, rapid, and extremely rapid under SSP1-2.6, SSP2-4.5 and SSP5-8.5, respectively. In addition, the changes in the Continental Basin and Songhua and Liaohe River Basin are larger than those at the mainland China scale. In the historical and future periods, the extreme temperature events that have a great influence on the Tmean at the Chinese mainland scale and different basin scales are all related to the minimum temperature. The findings from this study can provide references for formulating scientific and reasonable regional-scale climate change policies. [ABSTRACT FROM AUTHOR]

Eastern China is regularly exposed to extreme precipitation with significant socio-economical consequences. Following an observational analysis in a first part of this study, here the ability of the Coupled Model Intercomparison Project Phase 6 models to reproduce the main modes of interannual variability of 5-day summer extreme precipitation over eastern China is evaluated, using an empirical orthogonal teleconnection (EOT) method. These models capture the main patterns and magnitudes of the different EOT patterns, although the North China mode is less well represented. Models also reproduce the dynamical features associated with each mode. There is no systematic improvement in the ability of models to simulate either the pattern or the 5-day intensity when using higher resolution models compared to coarser resolution ones. Instead, multi-member or multi-model ensembles lead to results closer to observations. Using a low mitigation projection pathway (SSP-370), it is shown that the risk of the most extreme 5-day precipitation events by about 40%, 80% and more than 150% for global-mean warming levels, relative to 1850–1900, of + 1.5, + 2 and + 3 ∘ C respectively. This increase is found to be more significant for 5-days events than for seasonal scale precipitation, consistent with previous studies. [ABSTRACT FROM AUTHOR]

Water resources are essential for human beings. It is of significance to project future water resources carrying capacity for water resources planning and management. In this study, the Huang-Huai-Hai River Basin (HHHRB), where the contradiction between humans and water is prominent in China, is selected as the study area. The fuzzy comprehensive evaluation model of regional water resources carrying capacity is constructed, the variation characteristics of water resources affected by climate change are analyzed based on the Budyko-Fu model, and considering the influence of transit water resources and water diversion projects, the future water resources carrying capacity in HHHRB under four future climate scenarios in CMIP6 is projected. The results indicate that: (1) On the whole, the carrying capacity of water resources in HHHRB is weak, and the spatial difference is great. (2) Under the background of climate change in the future, precipitation, temperature, and water resources in HHHRB all show increasing trends with changes of 0.90–12.59%, 1.22–1.80 °C, and 13.12–34.29%. (3) Under the background of global change, the water resources carrying capacity of most prefecture-level cities in HHHRB will be greatly improved in the future, and the spatial distributions of change rates among different climate scenarios are relatively consistent. (4) The construction of water diversion projects such as the South-to-North Water Diversion Project has played an obvious role in improving the carrying capacity of water resources. The research results can provide important scientific and technological support for the rational allocation of water resources in the basin under the background of global change. [ABSTRACT FROM AUTHOR]

This study reports model assessments and future projections of the Coupled Model Intercomparison Project Phase 6 (CMIP6) models in simulating spring climate extremes over China and its eight subregions. The CMIP6 models are generally able to capture mean climate extremes and trends but have common deficiencies such as topography‐related cold‐temperature biases and precipitation overestimations. The CMIP6 multimodel median ensemble (MME) typically outperforms the individual models in simulating spring climate extremes. However, in terms of the model performance metrics, almost no models show better performance in reference to all indices compared to the observations. Furthermore, we investigate the future changes in these indices on national and regional scales over the near‐term (2021–2040), mid‐term (2041–2060), and long‐term (2081–2100) future periods relative to the 1995–2014 reference period under four Shared Socioeconomic Pathway (SSP) scenarios: SSP1‐2.6, SSP2‐4.5, SSP3‐7.0, and SSP5‐8.5. By the end of the 21st century, wet and warm extreme climate events are projected to increase, particularly in northern and western China. While dry and cold extreme climate events are projected to decrease, particularly in southern China. Additionally, the future projections of both temperature and precipitation extreme indices are remarkable under the high‐greenhouse‐gas (GHG)‐emission scenarios, with the projected changes exceeding those expected under the low‐GHG scenarios. [ABSTRACT FROM AUTHOR]

The Yellow River Basin is an important economic belt and key ecological reservation area in China. In the context of global warming, it is of great significance to project the drought disaster risk for ensuring water security and improving water resources management measures in practice. Based on the five Global Climate Models (GCMs) projections under three scenarios of the Shared Socioeconomic Pathways (SSP) (SSP126, SSP245, SSP585) released in the Sixth Coupled Model Intercomparison Project (CMIP6), this study analyzed the characteristics of meteorological drought in the Yellow River Basin in combination with SPEI indicators over 2015–2100. The result indicated that: (1) The GCMs from CMIP6 after bias correction performed better in reproducing the spatial and temporal variation of precipitation. The precipitation in the Yellow River Basin may exhibit increase trends from 2015 to 2100, especially under the SSP585 scenario. (2) The characteristics of meteorological drought in the Yellow River Basin varied from different combination scenarios. Under the SSP126 scenario, the meteorological drought will gradually intensify from 2040 to 2099, while the drought intensity under SSP245 and SSP585 scenarios will likely be higher than SSP126. (3) The spatial variation of meteorological drought in the Yellow River Basin is heterogeneous and uncertain in different combination scenarios and periods. The drought tendency in the Loess Plateau will increase significantly in the future, and the drought frequency and duration in the main water conservation areas of the Yellow River Basin was projected to increase. [ABSTRACT FROM AUTHOR]

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This paper explored the projected changes in precipitation, maximum temperature (T max), and minimum temperature (T min) over the Yangtze River Basin (YRB) based on 23 Global climate models (GCMs) from the Coupled Model Intercomparison Project phase 6 (CMIP6). The Empirical Quantile Mapping (EQM) approach was firstly applied to correct the biases in the GCMs. Next, the future changes were investigated by analyzing the multi-model ensemble (MME) of the bias-corrected dataset during 2025–2044 (near-term), 2045–2064 (mid-term), and 2081–2100 (long-term) periods, with reference to the baseline period 1995–2014, under three integrated scenarios (SSP1-2.6, SSP2-4.5, and SSP5-8.5) of the Shared Socioeconomic Pathways (SSPs) and the Representative Concentration Pathways (RCPs). Results show that: (1) the biases in CMIP6 GCMs can be effectively corrected by EQM, and the MME performs better than individual models for each climatic variable. (2) Precipitation over the YRB from 2025 to 2100 is projected to increase at the rate of 9.66 mm/decade, 13.45 mm/decade, and 21.01 mm/decade under SSP1‐2.6, SSP2‐4.5, and SSP5‐8.5, respectively. In the long-term, the annual precipitation is projected to increase by 10.41%, 10.66% and 15.80%, under SSP1‐2.6, SSP2‐4.5 and SSP5‐8.5, respectively. (3) T max (T min) over the YRB is projected to increase by 0.09 (0.07) °C/decade, 0.29 (0.27) °C/decade, and 0.66 (0.64) °C/decade under SSP1‐2.6, SSP2‐4.5, and SSP5‐8.5, respectively. In the long-term, T max (T min) averaged over the YRB is projected to increase by 1.75 (1.50) °C, 2.72 (2.54) °C and 5.04 (4.85) °C under SSP1‐2.6, SSP2‐4.5 and SSP5‐8.5, respectively. (4) Uncertainties in the projected precipitation and temperature over the YRB were reduced based on MME. But further researches, such as selecting the superior models with respect to the regional climate of the YRB from the CMIP6 and using the ensemble methods that assign weight based on the performance of each model, are still needed to provide more reliable climate projections. • Projected changes were explored by 23 CMIP6 GCMs under the three SSP-RCP scenarios. • Biases in the GCMs were corrected by Empirical Quantile Mapping (EQM) method. • Multi-model ensemble (MME) was used to reduce the uncertainties. [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]

Copyright of Advances in Atmospheric Sciences is the property of Springer Nature and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)