A new upgraded high-precision gridded precipitation dataset considering spatiotemporal and physical correlations for mainland China

Abstract. Precipitation is a critical driver of the water cycle, profoundly influencing water resources, agricultural productivity, and natural disasters. However, existing gridded precipitation datasets exhibit markable deficiencies in capturing the spatiotemporal and physical correlations of precipitation, which limits their accuracy, particularly in regions with sparse meteorological stations. Therefore, this study proposes a completely new gridded precipitation generation scheme to address these issues. The long-term daily observation from 3,476 gauges and incorporated 11 related precipitation variables were utilized to characterize the correlations of precipitation. By employing an improved inverse distance weighting method combined with the machine learning-based light gradient boosting machine (LGBM) algorithm, a new high-precision, long-term, daily gridded precipitation dataset for mainland China (CHM_PRE V2) was developed, which aims to improve upon and surpass the CHM_PRE V1 dataset, developed in our previous work. Validation against 63,397 high-density gauges demonstrated that CHM_PRE V2 significantly outperforms existing datasets, achieving a mean absolute error of 1.48 mm/day and a Kling-Gupta efficiency of 0.88, representing improvements of 12.84 % and 12.86 %, respectively, compared to the previously optimal dataset. Regarding precipitation event detection, CHM_PRE V2 achieved a Heidke skill score of 0.68 and a false alarm ratio of 0.24, surpassing other datasets by 17.24 % and 29.17 %, respectively. Feature importance analysis revealed that spatiotemporal and physical correlations contributed 37.10 %, 34.11 %, and 28.78 % to precipitation retrieval, underscoring the necessity of incorporating temporal and physical correlations. CHM_PRE V2 markedly enhances precipitation measurement accuracy, reduces overestimation of precipitation events, and provides a reliable foundation for hydrological modelling and climate assessments. This dataset features a resolution of 0.1°, spans from 1960 to 2023, and will be updated annually. Free access to the dataset can be found at https://doi.org/10.5281/zenodo.14632157 (Hu and Miao, 2025).