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In recent years, the warming–wetting trend in the arid region of Northwest China has attracted widespread attention. To reveal whether this phenomenon exists in the whole Pan-Central-Asia arid region, this paper adopts the latest monthly gridded dataset of the Climate Research Unit Time Series version 4.05 (CRU TS v4.05) and the multi-model ensemble data of the Coupled Model Intercomparison Project Phase 6 (CIMP6) for discussion from multiple perspectives. The results show that the Pan-Central-Asia arid region has been getting warmer and wetter in the last 80 years. Since the turn of the 21st century, there has been an apparent slowdown in the regional wetting trend despite the acceleration of precipitation increase, mainly caused by the growth in evapotranspiration potential. The interannual scale dominates the precipitation change, including significant quasi-three-year and quasi-six-year cycles. The interannual variability in precipitation is mainly affected by the change in the phases of Pacific decadal oscillation (PDO), while long-term variation dominates the temperature change, which is significantly related to the variations in the Arctic oscillation (AO). Thus, future research and predictions of regional precipitation should focus on the PDO variations, followed by the Southern Oscillation Index (SOI), whereas for research on, and predictions of, temperature, the effect of AO variations should be emphasized. Except for a few regions in Central-Eastern Mongolia and Central Kazakhstan, precipitation levels in most parts of the Pan-Central-Asia region have been increasing. The regional temperature exhibits a distribution pattern which decreases from northwest to southeast. The increase in precipitation in the Pan-Central-Asia arid region alleviates the drought in most regions, including most of Northwest China. However, the arid and semi-arid climate patterns in this region have not changed. The warming–wetting trend will significantly accelerate in medium-emissions scenarios in the next 80 years. Although the increase in precipitation may be a positive aspect of this trend, the rise in potential evapotranspiration caused by sharp warming may cause greater challenges to the regional climate and ecological environment. [ABSTRACT FROM AUTHOR]

Significant temporal and spatial variability in soil moisture (SM) is observed during the warm season in China and its surrounding regions. Because of the existence of two different evapotranspiration regimes, i.e., soil moisture-limited and energy-limited, averaging the land–atmosphere (L–A) coupling strength for all soil wetness scenarios may result in the loss of coupling signals. This study examines the daytime-only L–A interactions under different soil moisture conditions, by using two-legged metrics in the warm season from May to September 1981–2020, partitioning the interactions between SM and latent heat flux (SM–LH, the land leg) from the interactions between latent heat flux and the lifting condensation level (LH–LCL, the atmospheric leg). The statistical results reveal large regional differences in warm-season daytime L–A feedback in China and its surrounding areas. As the soil becomes wetter, the positive SM–LH coupling strength increases in arid regions (e.g., northwest China, Hetao, and the Great Indian Desert) and the positive feedback shifts to the negative one in semi-arid/semi-humid regions (northeast and northern China). The negative LH–LCL coupling is most pronounced in wet soil months in arid regions, while the opposite is true for the Tibetan Plateau. In terms of intraseasonal variation, the large variability of SM in north China, the Tibetan Plateau, and India due to the influence of the summer monsoon leads to the sign change in the land segment coupling index, comparing pre- and post-monsoon periods. To further examine the impact of SM anomalies on L–A coupling and to explore evapotranspiration regimes in the North China Plain, four sets of sensitivity experiments with different soil moisture levels over a period of 10 years were conducted. Under relatively dry soil conditions, evapotranspiration is dominated by the soil moisture-limited regime with positive L–A coupling, regardless of external moisture inflow. The critical soil moisture value separating a soil moisture-limited and an energy-limited regime lies between 0.24 m3/m3 and 0.29 m3/m3. Stronger positive feedback under negative soil moisture anomalies may increase the risk of drought in the North China Plain. [ABSTRACT FROM AUTHOR]

Located in the central part of the arid regions of Northwest China, the Heihe River Basin (HRB) plays an important role in wind prevention, sand fixation, and soil and water conservation as the second largest inland river basin. In the context of the warming and wetting climate observed in Northwest China, the situation of the ecological environment in the HRB is of significant concern. Using the data from meteorological observation stations, grid fusion and hydrological monitoring, this study analyzes the multi-scale climate changes in the HRB and their impacts on runoff. In addition, predictive models for runoff in the upper and middle reaches were developed using machine learning methods. The results indicate that the climate in the HRB has experienced an overall warming and wetting trend over the past 60 years. At the same time, there are clear regional variabilities in the climate changes. Precipitation shows decreasing trends in the northwestern part of the HRB, while it shows increases at rates higher than the regional average in the southeastern part. Moreover, the temperature increases are generally smaller in the upper reaches than those in the middle and lower reaches. Over the past 60 years, there has been a remarkable increase in runoff at the Yingluo Gorge (YL) hydrological station, which exhibits a distinct "single-peak" pattern in the variation of monthly runoff. The annual runoff volume at the YL (ZY) hydrological station is significantly correlated with the precipitation in the upper (middle) reaches, indicating the precipitation is the primary influencing factor determining the annual runoff. Temperature has a significant impact only on the runoff in the upper reaches, while its impact is not significant in the middle reaches. The models trained by the support vector machines and random forest models perform best in predicting the annual runoff and monthly runoff, respectively. This study can provide a scientific basis for environmental protection and sustainable development in the HRB. [ABSTRACT FROM AUTHOR]