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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]

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]