A new high-resolution multi-drought-index dataset for mainland China

Abstract. Drought indices are crucial for assessing and managing water scarcity and agricultural risks; however, the lack of a unified data foundation in existing datasets leads to inconsistencies that challenge the comparability of drought indices. This study is dedicated to creating CHM_Drought, an innovative and comprehensive long-term meteorological drought dataset with a spatial resolution of 0.1° and with data collected from 1961 to 2022 in mainland China. It features six pivotal meteorological drought indices: the standardized precipitation index (SPI), standardized precipitation evapotranspiration index (SPEI), evaporative demand drought index (EDDI), Palmer drought severity index (PDSI), self-calibrating Palmer drought severity index (SC-PDSI), and vapor pressure deficit (VPD), of which the SPI, SPEI, and EDDI contain multi-scale features for periods of 2 weeks and 1–12 months. The dataset features a comprehensive application of high-density meteorological station data and a complete framework starting from basic meteorological elements (the China Hydro-Meteorology dataset, CHM). Demonstrating its robustness, the dataset excels in accurately capturing drought events across mainland China, as evidenced by its detailed depiction of the 2022 summer drought in the Yangtze River basin. In addition, to evaluate CHM_Drought, we performed consistency tests with the drought indices calculated based on Climatic Research Unit (CRU) and CN05.1 data and found that all indices had high consistency overall and that the 2-week-scale SPI, SPEI, and EDDI had potential early-warning roles in drought monitoring. Overall, our dataset bridges the gap in high-precision multi-index drought data in China, and the complete CHM-based framework ensures the consistency and reliability of the dataset, which contributes to enhancing the understanding of drought patterns and trends in China. Free access to the dataset can be found at https://doi.org/10.5281/zenodo.14634773 (Zhang and Miao, 2025).