Water resources sustainability in High Mountain Asia (HMA) surrounding the Tibetan Plateau (TP)—known as Asia’s water tower—has triggered widespread concerns because HMA protects millions of people against water stress^1,2. However, the mechanisms behind the heterogeneous trends observed in terrestrial water storage (TWS) over the TP remain poorly understood. Here we use a Lagrangian particle dispersion model and satellite observations to attribute about 1 Gt of monthly TWS decline in the southern TP during 2003–2016 to westerlies-carried deficit in precipitation minus evaporation (PME) from the southeast North Atlantic. We further show that HMA blocks the propagation of PME deficit into the central TP, causing a monthly TWS increase by about 0.5 Gt. Furthermore, warming-induced snow and glacial melt as well as drying-induced TWS depletion in HMA weaken the blocking of HMA’s mountains, causing persistent northward expansion of the TP’s TWS deficit since 2009. Future projections under two emissions scenarios verified by satellite observations during 2020–2021 indicate that, by the end of the twenty-first century, up to 84% (for scenario SSP245) and 97% (for scenario SSP585) of the TP could be afflicted by TWS deficits. Our findings indicate a trajectory towards unsustainable water systems in HMA that could exacerbate downstream water stress.

Amplifying landslide hazards in the backdrop of warming climate and intensifying human activities calls for an integrated framework for accurately evaluating landslide susceptibility at fine spatiotemporal resolutions, which is critical for the mitigation of increasingly high landslide disaster risks. Yet, dynamic landslide susceptibility mapping is still lacking. Using high-quality data, from 14,435 landslides and non-landslides, we developed an efficient holistic framework for evaluating landslide susceptibility, considering landslide-relevant internal and external factors based on cloud computing platform and algorithmic models, which enables dynamic updating of a landslide susceptibility map at the regional scale, particularly in regions with highly complicated topographical features such as the Hengduan Mountains, as considered in this study. We compared Classification and Regression Trees (CART), Support Vector Machines (SVM), and Random Forest (RF) classifiers to screen out the best portfolio model for landslide susceptibility mapping on the Google Earth Engine (GEE) platform. We found that the Random Forest (RF) classifier integrated with synergy mode had the best modeling performance, with 90.48% and 89.24% accuracy and precision, respectively. We also found that forests and grasslands had the controlling effect on the occurrence of landslides, while human activities had a notable inducing effect on the occurrence of landslides within the Hengduan Mountains. This study highlights the performance of the holistic landslide susceptibility evaluation framework proposed in this study and provides a viable technique for landslide susceptibility evaluation in other regions of the globe.

Drylands with fragile ecosystems and severe water shortages are particularly vulnerable to climatic change. Northwestern China (NWC), a typical arid region, faces uncertainty regarding future wetting or drying trends. A comprehensive assessment and projection of these conditions are crucial for water resource management. In this study, we employ a Lagrangian trajectory model, optimal fingerprint analysis, and a maximum covariance technique to evaluate wetting and/or drying trends in NWC over the historical (1981–2023) and future (2024–2099) periods. Our results show that over 80% of NWC experienced increases in air temperature, precipitation, and evaporation during the historical period. External and internal water vapor sources contribute 92% and 8%, respectively, to precipitation changes. Incoming water vapor predominantly originated from the North Atlantic (31.9%) and the South China Sea ‐ Bay of Bengal region (39.3%), with a strong positive correlation (r = 0.71) between Atlantic sea surface temperatures and precipitation minus evaporation in NWC. Water vapor enters NWC from the southern, northern, and western boundaries, while 83.4% escapes through the eastern boundary. The precipitation trend is strongly influenced by the combined effects of anthropogenic and natural forcings, accounting for 36.8% to the observed increase. Under a 1.5°C warming scenario, warming‐wetting regions shift northward, whereas higher warming levels (2°C, 3°C, 4°C) cause these regions to shift southeastward and shrink. Our findings underscore NWC's high sensitivity to climate warming and highlight the pressing challenge of water security in a warming world.