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 2026-08-19T16:25:25.236Z 622.1 2026-08-19T16:25:40.587Z 622.1 2026-08-19T16:25:55.531Z 622.1 2026-08-19T16:26:10.396Z 622.1 2026-08-19T16:26:26.656Z 622.1 2026-08-19T16:26:43.275Z 622.1 2026-08-19T16:26:56.788Z 622.1 2026-08-19T16:27:10.855Z 622.1 2026-08-19T16:27:26.976Z 622.1 2026-08-19T16:27:42.994Z 622.1 2026-08-19T16:27:58.510Z 622.1 2026-08-19T16:28:13.299Z 622.1 2026-08-19T16:28:28.044Z 622.1 2026-08-19T16:28:42.738Z 622.1 2026-08-19T16:28:58.298Z 622.1 2026-08-19T16:29:13.131Z 622.1 2026-08-19T16:29:28.212Z 622.1 2026-08-19T16:29:43.067Z 622.1 2026-08-19T16:29:57.422Z 622.1 2026-08-19T16:30:13.206Z 622.1 2026-08-19T16:30:29.625Z 622.1 2026-08-19T16:30:44.646Z 622.1 2026-08-19T16:31:00.464Z 622.1 2026-08-19T16:31:15.007Z 622.1 2026-08-19T16:31:30.185Z 622.1 2026-08-19T16:31:32.082Z 622.1 2026-08-19T16:31:33.979Z 622.1 2026-08-19T16:31:35.531Z 622.1 2026-08-19T16:31:49.322Z 622.1 2026-08-19T16:32:04.986Z