Deciphering Spatiotemporal Dynamics of Vegetation Drought Resilience in China

Under accelerated global warming, frequent droughts pose mounting threats to vegetation productivity, yet the spatiotemporal patterns and primary controls of drought resilience (DR) in China remain insufficiently quantified. This study aimed to characterize DR trends across Köppen–Geiger climate zones in China from 2001 to 2020 and to identify the dominant drivers and their interactions. We constructed a hazard–exposure–adaptability framework, combining multi-source satellite observations and the station data. A Bayesian-optimized Light Gradient Boosting Machine (LightGBM, version 4.3.0) model was trained under five-fold cross-validation. Shapley Additive exPlanations (SHAP) analysis decomposed each driver’s main and interaction effects on DR. The results indicated that DR was better in tropical regions, whereas arid and polar regions require more attention. From 2001 to 2020, 45.3% of China’s land area saw DR increases, while 36.4% declined. The key drivers influencing DR were temperature, sunlight hours, potential evapotranspiration, and precipitation. Notably, an increase in sunlight hours was often accompanied by a decrease in precipitation, resulting in suboptimal DR in China. When the normalized precipitation fell within the range of 0.12 to 0.65, elevated temperature exhibited an inhibitory effect on DR. Overall, this study established a DR assessment framework, elucidated its spatiotemporal dynamics, and revealed key driver interactions, offering timely insights for ecosystem research and management in the face of climate change.