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 2026-07-22T12:48:30.061Z 622.1 2026-07-22T12:48:42.285Z 622.1 2026-07-22T12:48:53.220Z 622.1 2026-07-22T12:49:05.758Z 622.1 2026-07-22T12:49:17.796Z 622.1 2026-07-22T12:49:29.305Z 622.1 2026-07-22T12:49:41.158Z 622.1 2026-07-22T12:49:52.714Z 622.1 2026-07-22T12:50:05.412Z 622.1 2026-07-22T12:50:18.513Z 622.1 2026-07-22T12:50:29.810Z 622.1 2026-07-22T12:50:41.599Z 622.1 2026-07-22T12:50:52.724Z 622.1 2026-07-22T12:51:04.296Z 622.1 2026-07-22T12:51:16.112Z 622.1 2026-07-22T12:51:29.634Z 622.1 2026-07-22T12:51:42.104Z 622.1 2026-07-22T12:51:54.558Z 622.1 2026-07-22T12:52:05.979Z 622.1 2026-07-22T12:52:17.987Z 622.1 2026-07-22T12:52:29.929Z 622.1 2026-07-22T12:52:40.658Z 622.1 2026-07-22T12:52:52.756Z 622.1 2026-07-22T12:53:03.937Z 622.1 2026-07-22T12:53:16.159Z 622.1 2026-07-22T12:53:27.970Z 622.1 2026-07-22T12:53:39.934Z 622.1 2026-07-22T12:53:51.297Z 622.1 2026-07-22T12:54:03.239Z 622.1 2026-07-22T12:54:15.702Z 622.1 2026-07-22T12:54:27.036Z