GMCP: A Fully Global Multisource Merging-and-Calibration Precipitation Dataset (1-Hourly, 0.1°, Global, 2000–the Present)

作者：Ziqiang Ma, Jintao Xu, Bo Dong, Xie Hu, Hao Hu, Songkun Yan, Siyu Zhu, Kang He, Zhou Shi, Yun Chen, Xiang Fang, Qinghong Zhang, Songyan Gu, Fuzhong Weng · 发表于：Bulletin of the American Meteorological Society · 年份：2025 · DOI：10.1175/bams-d-24-0051.1 · 被引用次数：26 · 研究领域：Climate variability and models、Meteorological Phenomena and Simulations、Cryospheric studies and observations

Abstract Current global multisource merged precipitation datasets can facilitate better utilization of the complementary nature of gauge-, satellite-, and reanalysis-based precipitation estimates, particularly for capturing precipitation variability. However, merging these datasets at high resolutions of 1-hourly and 0.1° on a full global scale remains a substantial challenge for the scientific community owing to high spatiotemporal heterogeneities. This study proposes a merging-and-calibration framework to optimally integrate the advantages of gauge-, satellite-, and model-based precipitation estimates, focusing on precipitation occurrences and providing a new fully global multisource merging-and-calibration precipitation (GMCP: 1-hourly, 0.1°, global, 2000–the present) dataset. The main conclusions included 1) GMCP generally outperformed the input datasets, ERA5-Land, GSMaP–moving vector with Kalman filter (MVK), and IMERG-Late, across various spatiotemporal scales, both in regional statistics and extreme precipitation systems; 2) GMCP significantly outperformed IMERG-Final, calibrated by gauge analysis at the monthly scale, with the improvements in correlation coefficient (CC), root-mean-square error (RMSE), and Heidke skill score (HSS) by approximately 66.67%, 39.25%, and 26.83%, respectively, from 2016 to 2020 over the contiguous United States (CONUS); 3) compared to the state-of-the-art multisource merged product with a daily gauge correction scheme, Multisource Weighte...