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

作者：Jinlong Hu, Chiyuan Miao, Jiajia Su, Qi Zhang, Jiaojiao Gou, Qiaohong Sun · 发表于：Earth system science data · 年份：2025 · DOI：10.5194/essd-17-3987-2025 · 被引用次数：47 · 研究领域：Precipitation Measurement and Analysis、Climate variability and models、Meteorological Phenomena and Simulations

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 spatial autocorrelation and associated environmental and climatic influences – here referred to collectively as precipitation-related covariates – that limit their accuracy, particularly in regions with sparse meteorological stations. To address these challenges, this study proposes a completely new gridded precipitation generation scheme that integrates long-term daily observations from 3746 gauges with 11 key precipitation-related covariates. Building upon the improved inverse distance weighting interpolation method used in our previous dataset CHM_PRE V1, we integrated a machine learning algorithm – light gradient boosting machine (LGBM) – to incorporate precipitation-related covariates in a data-driven manner. This integration allows for a more comprehensive characterization of precipitation patterns, jointly capturing spatial autocorrelation and covariate-based variability. By this novel scheme, a new high-precision, long-term, daily gridded precipitation dataset for the Chinese mainland (CHM_PRE V2) was developed. Validation against 63 397 high-density gauges demonstrated that CHM_PRE V2 significantly outperforms existing gridded precipitation datasets. Specifically, it achieves a mean absolute error of 1.48 mm d−1 and a Kl...