CHM_PRE V2: An upgraded high-precision gridded precipitation dataset for the Chinese mainland considering spatial autocorrelation and covariates

Important Notice: These old dataset versions (V2.0.x) have been superseded. A new version, CHM_PRE V2.1, is now available and is recommended for all users (https://doi.org/10.5281/zenodo.14632156). The updated version (V2.1) extends the data coverage to 2024 and incorporates adjusted precipitation values for the southern foothills of the Himalayas.

The CHM_PRE V2 dataset is a new high-precision, long-term, daily gridded precipitation dataset for mainland China. The long-term daily observation from 3,476 gauges and incorporated 11 related precipitation variables were utilized to characterize the correlations of precipitation. Then, the dataset was developed by employing an improved inverse distance weighting method combined with the machine learning-based light gradient boosting machine (LGBM) algorithm. CHM_PRE V2 demonstrates strong spatiotemporal consistency with existing gridded precipitation datasets, including CHM_PRE V1, GSMaP, IMERG, PERSIANN-CDR, and GLDAS. Validation against 63,397 high-density gauges confirms its high accuracy in both precipitation values and events. The dataset achieves a mean absolute error of 1.48 mm/day and a Kling-Gupta efficiency coefficient of 0.88. In terms of event detection capability, CHM_PRE V2 achieves a Heidke skill score of 0.68 and a false alarm ratio of 0.24. Overall, CHM_PRE V2 significantly enhances precipitation measurement accuracy and reduces the overestimation of precipitation events, providing a reliable foundation for hydrological modeling and climate assessments. The CHM_PRE V2 dataset provides daily precipitation data with a resolution of 0.1°, covering the entire mainland China (18°N–54°N, 72°E–136°E). This dataset covers the period of 1960–2023, and will be continuously updated annually. The daily precipitation data is provided in NetCDF format, and for the convenience of users, we also offer annual and monthly total precipitation data in both NetCDF and GeoTIFF formats.