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 2026-07-26T23:51:13.447Z 622.1 2026-07-26T23:51:14.070Z 622.1 2026-07-26T23:51:14.768Z 622.1 2026-07-26T23:51:15.416Z 622.1 2026-07-26T23:51:16.236Z 622.1 2026-07-26T23:51:17.102Z 622.1 2026-07-26T23:51:17.783Z 622.1 2026-07-26T23:51:18.418Z 622.1 2026-07-26T23:51:19.099Z 622.1 2026-07-26T23:51:19.760Z 622.1 2026-07-26T23:51:20.426Z 622.1 2026-07-26T23:51:21.145Z 622.1 2026-07-26T23:51:21.930Z 622.1 2026-07-26T23:51:22.571Z 622.1 2026-07-26T23:51:23.256Z 622.1 2026-07-26T23:51:24.002Z 622.1 2026-07-26T23:51:24.673Z 622.1 2026-07-26T23:51:33.596Z 622.1 2026-07-26T23:51:46.153Z 622.1 2026-07-26T23:52:00.533Z 622.1 2026-07-26T23:52:15.754Z 622.1 2026-07-26T23:52:30.893Z 622.1 2026-07-26T23:52:45.083Z 622.1 2026-07-26T23:52:59.387Z 622.1 2026-07-26T23:53:15.242Z 622.1 2026-07-26T23:53:30.017Z 622.1 2026-07-26T23:53:44.286Z 622.1 2026-07-26T23:53:59.741Z 622.1 2026-07-26T23:54:13.085Z 622.1 2026-07-26T23:54:27.741Z 622.1 2026-07-26T23:54:41.823Z