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 2026-07-24T19:33:51.336Z 622.1 2026-07-24T19:34:03.654Z 622.1 2026-07-24T19:34:19.092Z 622.1 2026-07-24T19:34:44.209Z 622.1 2026-07-24T19:35:00.328Z 622.1 2026-07-24T19:35:14.316Z 622.1 2026-07-24T19:35:28.452Z 622.1 2026-07-24T19:35:42.755Z 622.1 2026-07-24T19:35:58.280Z 622.1 2026-07-24T19:36:14.173Z 622.1 2026-07-24T19:36:28.812Z 622.1 2026-07-24T19:36:43.731Z 622.1 2026-07-24T19:36:57.444Z 622.1 2026-07-24T19:37:10.542Z 622.1 2026-07-24T19:37:22.755Z 622.1 2026-07-24T19:37:37.011Z 622.1 2026-07-24T19:37:50.566Z 622.1 2026-07-24T19:38:04.930Z 622.1 2026-07-24T19:38:18.815Z 622.1 2026-07-24T19:38:31.891Z 622.1 2026-07-24T19:38:46.146Z 622.1 2026-07-24T19:38:59.066Z 622.1 2026-07-24T19:39:11.795Z 622.1 2026-07-24T19:39:25.084Z 622.1 2026-07-24T19:39:39.652Z 622.1 2026-07-24T19:39:53.438Z 622.1 2026-07-24T19:40:07.245Z 622.1 2026-07-24T19:40:19.645Z 622.1 2026-07-24T19:40:32.822Z 622.1 2026-07-24T19:40:46.765Z 622.1 2026-07-24T19:41:00.946Z