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 2026-07-21T03:23:06.830Z 622.1 2026-07-21T03:23:27.403Z 622.1 2026-07-21T03:23:50.574Z 622.1 2026-07-21T03:24:06.730Z 622.1 2026-07-21T03:24:24.377Z 622.1 2026-07-21T03:24:47.263Z 622.1 2026-07-21T03:25:07.477Z 622.1 2026-07-21T03:25:25.923Z 622.1 2026-07-21T03:25:45.908Z 622.1 2026-07-21T03:26:01.793Z 622.1 2026-07-21T03:26:21.595Z 622.1 2026-07-21T03:26:38.092Z 622.1 2026-07-21T03:26:58.033Z 622.1 2026-07-21T03:27:14.488Z 622.1 2026-07-21T03:27:29.669Z 622.1 2026-07-21T03:27:47.139Z 622.1 2026-07-21T03:28:14.884Z 622.1 2026-07-21T03:28:32.373Z 622.1 2026-07-21T03:28:49.761Z 622.1 2026-07-21T03:29:09.054Z 622.1 2026-07-21T03:29:30.359Z 622.1 2026-07-21T03:29:45.485Z 622.1 2026-07-21T03:30:13.021Z 622.1 2026-07-21T03:30:29.771Z 622.1 2026-07-21T03:30:45.314Z 622.1 2026-07-21T03:31:01.074Z 622.1 2026-07-21T03:31:15.826Z 622.1 2026-07-21T03:31:37.594Z 622.1 2026-07-21T03:31:55.377Z 622.1 2026-07-21T03:32:12.364Z