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 2026-07-21T21:02:08.462Z 622.1 2026-07-21T21:02:24.190Z 622.1 2026-07-21T21:02:40.112Z 622.1 2026-07-21T21:02:54.210Z 622.1 2026-07-21T21:03:07.127Z 622.1 2026-07-21T21:03:22.596Z 622.1 2026-07-21T21:03:37.264Z 622.1 2026-07-21T21:03:52.768Z 622.1 2026-07-21T21:04:06.892Z 622.1 2026-07-21T21:04:21.905Z 622.1 2026-07-21T21:04:37.132Z 622.1 2026-07-21T21:04:51.615Z 622.1 2026-07-21T21:05:06.083Z 622.1 2026-07-21T21:05:19.700Z 622.1 2026-07-21T21:05:33.644Z 622.1 2026-07-21T21:05:48Z 622.1 2026-07-21T21:06:01.472Z 622.1 2026-07-21T21:06:14.939Z 622.1 2026-07-21T21:06:28.620Z 622.1 2026-07-21T21:06:42.838Z 622.1 2026-07-21T21:06:56.537Z 622.1 2026-07-21T21:07:10.917Z 622.1 2026-07-21T21:07:24.764Z 622.1 2026-07-21T21:07:38.858Z 622.1 2026-07-21T21:07:53.300Z 622.1 2026-07-21T21:08:07.592Z 622.1 2026-07-21T21:08:21.946Z 622.1 2026-07-21T21:08:36.424Z 622.1 2026-07-21T21:08:51.064Z 622.1 2026-07-21T21:09:06.330Z 622.1 2026-07-21T21:09:20.131Z