Anthropogenic impacts on widespread global soil moisture (SM) drying in the root zone layer during 1948–2005 were evaluated based on the Global Land Data Assimilation System version 2 (GLDAS‐2) and global climate models from the Coupled Model Intercomparison Project Phase 5 using trend analysis and optimal fingerprint methods. Both methods show agreement that natural forcing alone cannot drive significant SM drying. There is a high probability (≥90%) that the anthropogenic climate change signal is detectable in global SM drying. Specifically, anthropogenic greenhouse gas forcing can lead to global SM drying by 2.1 × 10⁻³ m³/m³, which is comparable to the drying trend seen in Global Land Data Assimilation System version 2 (2.4 × 10⁻³ m³/m³) over the past 58 years. Global SM drying is expected to continue in the future, given continuous greenhouse gas emissions.

The partitioning of precipitation into runoff (R) and evapotranspiration (E), governed by the controlling parameter in the Budyko framework (i.e., n parameter in the Choudhury and Yang equation), is critical to assessing the water balance at global scale. It is widely acknowledged that the spatial variation in this controlling parameter is affected by landscape characteristics, but characterizing its temporal variation remains yet to be done. Considering effective precipitation (P_e), the Budyko framework was extended to the annual water balance analysis. To reflect the mismatch between water supply (precipitation, P) and energy (potential evapotranspiration, E₀), we proposed a climate seasonality and asynchrony index (SAI) in terms of both phase and amplitude mismatch between P and E₀. Considering streamflow changes in 26 large river basins as a case study, SAI was found to the key factor explaining 51 % of the annual variance of parameter n. Furthermore, the vegetation dynamics (M) remarkably impacted the temporal variation in n, explaining 67 % of the variance. With SAI and M, a semi-empirical formula for parameter n was developed at the annual scale to describe annual runoff (R) and evapotranspiration (E). The impacts of climate variability (P_e, E₀ and SAI) and M on R and E changes were then quantified. Results showed that R and E changes were controlled mainly by the P_e variations in most river basins over the globe, while SAI acted as the controlling factor modifying R and E changes in the East Asian subtropical monsoon zone. SAI, M and E₀ have larger impacts on E than on R, whereas P_e has larger impacts on R.

Soil moisture (SM) is a key hydrological component regulating the net ecosystem energy exchange at the land‐atmosphere boundary layer over the continents via heat fluxes and relevant feedback on precipitation. Due to its ecological and meteor‐hydrological implications, SM change is of great significance in Eurasia that has the highest population density and fragile ecological environment. Using monthly data from the Global Land Data Assimilation System, this study investigated SM changes over Eurasia during the warm season (May–September). It was found that recent 63 years witnessed widespread decreasing SM across Eurasia during the warm season. Regions with a drying SM tendency kept expanding till the 1990s. Specifically, the largest decreasing magnitude of SM with the aridity index ranging 0.5–0.6 and 1.0–1.1 was found along the semi‐arid and dry‐humid transition regions, respectively. In addition, more significant drying SM was observed in Sahel, northern Asia, northeastern Asia, and western Europe. Weakening West African monsoon (WAM)/East Asia summer monsoon did not benefit the propagation of water vapor flux to the Sahel regions/northeastern and northern Asia, and hence decreased SM in these regions. Besides, results by the maximum covariance analysis highlighted the roles of warming climate in SM variations over Eurasia during the warm season. Global climate models also indicate decreased SM due to global warming and projects continuously decreasing SM in the warm season over the 21st century under Representative Concentration Pathway (RCP)2.6, RCP4.5, RCP6.0, and RCP8.5 scenarios. Decreasing SM across the Eurasia and related ecological and environmental implications should cause international concern.

Drought is a major natural hazard that has massive impacts on the society. How to monitor drought is critical for its mitigation and early warning. This study proposed a modified version of the multivariate standardized drought index (MSDI) based on precipitation, evapotranspiration, and soil moisture, i.e., modified multivariate standardized drought index (MMSDI). This study also used nonparametric joint probability distribution analysis. Comparisons were done between standardized precipitation evapotranspiration index (SPEI), standardized soil moisture index (SSMI), MSDI, and MMSDI, and real‐world observed drought regimes. Results indicated that MMSDI detected droughts that SPEI and/or SSMI failed to do. Also, MMSDI detected almost all droughts that were identified by SPEI and SSMI. Further, droughts detected by MMSDI were similar to real‐world observed droughts in terms of drought intensity and drought‐affected area. When compared to MMSDI, MSDI has the potential to overestimate drought intensity and drought‐affected area across China, which should be attributed to exclusion of the evapotranspiration components from estimation of drought intensity. Therefore, MMSDI is proposed for drought monitoring that can detect agrometeorological droughts. Results of this study provide a framework for integrated drought monitoring in other regions of the world and can help to develop drought mitigation.