all edu.ucar.gdex GRID NetCDF Freely Available Current global multi-source 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 degree on a full global scale remains a substantial challenge for the scientific community due to high spatiotemporal heterogeneities. This study proposed 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 multi-source Merging-and-Calibration Precipitation dataset (GMCP: 1-hourly, 0.1 degree, global, 2000-Present). The main conclusions included: (1) GMCP generally outperformed the input datasets, ERA5-Land, GSMaP-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 Continental United States (CONUS); (3) compared to the state-of-the-art multi-source merged product with a daily gauge correction scheme, MSWEP V2 (3-hourly and 0.1 degree), GMCP demonstrated the notable improvements with an approximately 20% enhancement in accurately capturing the precipitation occurrences against approximately 67,000 rain gauges over Mainland China in 2016; (4) in comparison to another well-known multi-source merged quasi-global daily and 0.05 degree precipitation product, CHIPRS integrating the gauge-, satellite-, and reanalysis-based precipitation estimates, GMCP also demonstrated the notable improvements at the daily scale, achieving the increases in CC, RMSE, and HSS by around 57.45%, 38.18%, and 75.76%, respectively, against approximately 67,000 rain gauges over Mainland China in 2016; and (5) this framework was suitable for generating the fully global precipitation datasets at 1-hourly and 0.1 degree scales, significantly mitigating the inherent drawbacks of each input dataset, with GMCP demonstrating the great potential as a valuable resource for worldwide scientific research and societal applications. CN/CAS/TPDC NCAR/GDEX 622.1 2026-07-22T12:18:34.530Z 622.1 2026-07-22T12:18:50.476Z 622.1 2026-07-22T12:19:03.994Z 622.1 2026-07-22T12:19:19.576Z 622.1 2026-07-22T12:19:33.956Z 622.1 2026-07-22T12:19:47.147Z 622.1 2026-07-22T12:20:00.224Z 622.1 2026-07-22T12:20:13.531Z 622.1 2026-07-22T12:20:26.454Z 622.1 2026-07-22T12:20:40.329Z 622.1 2026-07-22T12:20:53.989Z 622.1 2026-07-22T12:21:09.363Z 622.1 2026-07-22T12:21:23.418Z 622.1 2026-07-22T12:21:37.905Z 622.1 2026-07-22T12:21:52.410Z 622.1 2026-07-22T12:22:06.060Z 622.1 2026-07-22T12:22:19.683Z 622.1 2026-07-22T12:22:33.847Z 622.1 2026-07-22T12:22:47.470Z 622.1 2026-07-22T12:23:01.310Z 622.1 2026-07-22T12:23:13.826Z 622.1 2026-07-22T12:23:28.174Z 622.1 2026-07-22T12:23:42.596Z 622.1 2026-07-22T12:23:56.344Z 622.1 2026-07-22T12:24:08.823Z 622.1 2026-07-22T12:24:21.918Z 622.1 2026-07-22T12:24:35.668Z 622.1 2026-07-22T12:24:49.038Z 622.1 2026-07-22T12:25:01.464Z 622.1 2026-07-22T12:25:14.158Z 622.1 2026-07-22T12:25:28.360Z