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 2026-07-22T08:05:07.875Z 622.1 2026-07-22T08:05:24.215Z 622.1 2026-07-22T08:05:42.079Z 622.1 2026-07-22T08:05:57.913Z 622.1 2026-07-22T08:06:13.658Z 622.1 2026-07-22T08:06:28.397Z 622.1 2026-07-22T08:06:43.278Z 622.1 2026-07-22T08:06:59.008Z 622.1 2026-07-22T08:07:15.576Z 622.1 2026-07-22T08:07:30.931Z 622.1 2026-07-22T08:07:46.936Z 622.1 2026-07-22T08:08:00.654Z 622.1 2026-07-22T08:08:14.723Z 622.1 2026-07-22T08:08:31.686Z 622.1 2026-07-22T08:08:47.430Z 622.1 2026-07-22T08:09:02.469Z 622.1 2026-07-22T08:09:17.834Z 622.1 2026-07-22T08:09:31.993Z 622.1 2026-07-22T08:09:47.746Z 622.1 2026-07-22T08:10:05.420Z 622.1 2026-07-22T08:10:18.407Z 622.1 2026-07-22T08:10:33.098Z 622.1 2026-07-22T08:10:47.147Z 622.1 2026-07-22T08:11:01.970Z 622.1 2026-07-22T08:11:17.627Z 622.1 2026-07-22T08:11:31.883Z 622.1 2026-07-22T08:11:46.949Z 622.1 2026-07-22T08:12:02.504Z 622.1 2026-07-22T08:12:18.341Z 622.1 2026-07-22T08:12:34.665Z 622.1 2026-07-22T08:12:49.506Z