Identifying Optimal Reanalysis and Remote Sensing Data Combinations for Multi-Scale SPEI-Based Drought Assessment in Zhejiang Province, China

Accurate drought assessment is crucial for effective regional water resource management. While reanalysis and remote sensing products enable high-resolution drought assessment, their regional application requires rigorous local validation. This study evaluates nine data combinations, pairing three precipitation products with three evapotranspiration products, to identify the optimal combination for robust SPEI estimation and subsequently to investigate the spatiotemporal variations in drought conditions during 1980–2020 in Zhejiang Province, China. The results indicate that the choice of precipitation product is the dominant factor influencing SPEI accuracy, with the combination of CMFD V2.0 precipitation and GLEAM v4.2a evapotranspiration identified as the most reliable for SPEI estimation across multiple timescales (SPEI1/3/6/12). The long-term trend analysis of the SPEI derived from this optimal data combination reveals significant spatiotemporal heterogeneity: temporally, a pronounced “wetter winters, drier springs” seasonal pattern emerges, posing a substantial threat to agricultural water security; spatially, a distinct divergence shows central/northeastern areas wetting while southern/southeastern regions experience a significant drying trend, particularly for long-term hydrological drought (SPEI12). Additionally, the prevalence of light droughts across the province suggests a sustained baseline of water stress. Attribution analysis further demonstrates that precipitation is the dominant driver of droughts across all timescales. This study contributes both a validated, high-resolution data foundation for regional drought assessment and a scientific basis for targeted drought adaptation strategies.