Vapor pressure deficit (VPD) and soil moisture (SM) are the two main parameters related to ecosystem water stresses. They are tightly coupled through land–atmosphere interactions and have large impacts on terrestrial ecosystems and global water and carbon cycles. However, the coupling characteristics between the two have not been thoroughly studied, particularly in the context of climate warming. In this study, based on remote sensing and reanalysis datasets, spatial and temporal variations in the VPD and SM and their correlation coefficients in the growing season in China from 1982 to 2018 were evaluated. Then, the probabilities of compound water stress (high VPD and low SM) were investigated under three scenarios based on a copula analysis. The results show that, over the 37 years, the VPD significantly increased during the growing season, while the SM significantly decreased. The coupling relationship between the VPD and SM was relatively weak in extreme arid and arid regions. In contrast, this relationship was stronger in semi-arid and semi-humid regions, where the probabilities of compound water stress were significantly higher (p < 0.05). The probabilities of compound water stress (high VPD and low SM) were significantly higher than the probabilities when the VPD and SM were independent, and this difference increased with the severity of the water stress in the same region. The obtained results can be further applied to improve Earth system models and formulate agricultural irrigation schemes.

Air pollution, particularly fine particulate matter (PM_2.5) and ozone (O₃) pollution, poses serious challenges to environmental quality and sustainable development. The Tibetan Plateau, often described as the “Third Pole,” functions as a key ecological shield for China and exerts wide-reaching influence on global climate systems, hydrological cycles, and cross-regional pollution transport. To better clarify the driving mechanisms of air pollution in this sensitive region, we propose an integrated MRG–HSW framework, which, for the first time, systematically couples statistical modeling and trajectory analysis by combining multivariate regression, residual-based screening, and HYSPLIT–WCWT trajectory analyses. Taking Qinghai Province as a case study, ERA5 and GDAS1 reanalysis products were coupled with in situ monitoring to identify the relative contributions of local emissions and long-range atmospheric transport. The results show that, in low-elevation zones, PM_2.5 levels are largely governed by local anthropogenic activities (R² = 0.631–0.803), whereas O₃ concentrations respond more strongly to meteorological variability (R² = 0.529–0.779). At higher elevations, however, local explanatory factors weaken, and long-range transport from the Hexi Corridor, Qaidam Basin, and even South Asia becomes the dominant influence for both pollutants. Additional sensitivity tests confirm that the framework performs robustly under diverse meteorological and seasonal conditions. Collectively, this work not only establishes a transferable methodology for source attribution in plateau environments but also underscores the pivotal role of the Tibetan Plateau in sustaining regional air quality and global environmental stability.