Merge Satellite and Gauge Daily Precipitation Data for Areas with Sparse-Gauge and Rugged Terrain: A High-Accuracy Gridded Dataset CGS01 (2000-2024) across China

Bokun Yan, Wenpeng Li, Fuping Gan, Yuejun Zheng, Jun Bai, Longfeng Wang, Jianrong Li, Yi Guo, Chunyan Li, Naichen Xing, Yue Zhuo, Ruoyi Li

High-precision quantiffcation of regional precipitation is the foundation of watershed water balance analysis and water resources management. However, current approaches are hindered by methodological diversity and inconsistent results, with limited accuracy, especially in gauge-sparse regions and areas with drastic topographic relief. This study proposes a dual thin-plate spline (TPS) fusion method combining satellite observations and ground gauge observations to develop a daily 5 precipitation dataset named CGS01. Multi-dimensional comparisons and comprehensive accuracy evaluations are conducted against several mainstream precipitation products including CN05.1, CMFD_V1 and CHM_PRE_V2. From a methodological perspective, this paper analyzes the core difffculties and feasible solutions for regional precipitation estimation, and discusses the error sources of GPM satellite precipitation data, as well as the application potential of the proposed method in watershed water balance assessment and high-precision global precipitation quantiffcation. The results reveal because of prominent spatial 10 heterogeneity in precipitation, reliance merely on ground gauge monitoring cannot accurately depict the spatial distribution of regional precipitation, making it essential to integrate high-frequency spatial variation information of precipitation with in-situ gauge data. Compared with digital elevation model (DEM) and reanalysis data used as covariates, satellite precipitation data
exhibits higher reliability when serving as covariates in TPS method, while corrections for systematic errors and suppression of random errors are still required. A novel systematic error correction method is proposed. Adopting only 27% of the gauge 15 numbers used in previous studies, the newly developed CGS01 presents obviously superior performance to existing datasets. The national average error of annual precipitation is 2%. Validated against observed runoff data, the watershed water balance analysis error is 10.9%. Furthermore, CGS01 can reasonably reffect the controlling effects of topography and wind direction on precipitation spatial patterns in high mountain and canyon areas without in-situ observations. Compared with CHM_PRE_V2, CGS01 daily precipitation shows improvements of 14.2%, 2.9%, 21.3%, and 8.2% in the correlation coefffcient, Kling-Gupta 20 efffciency, probability of detection, and mean absolute error. The CGS01 dataset shows that from 2000 to 2024 eight out of China’s ten ffrst level watersheds showed annually wetting trends, with trends ranging from 0.5 to 8.4 mm per year, and in seven of the ten ffrst level watersheds, the contribution ratio of daily extreme precipitation to total precipitation increased at a rate of 0.2%-0.5% per year. Free access to the dataset can be found at https://doi.org/10.5281/zenodo.20176004.