{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,9]],"date-time":"2026-03-09T12:54:44Z","timestamp":1773060884260,"version":"3.50.1"},"reference-count":60,"publisher":"MDPI AG","issue":"18","license":[{"start":{"date-parts":[[2023,9,6]],"date-time":"2023-09-06T00:00:00Z","timestamp":1693958400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Major Program of the National Natural Science Foundation of China","award":["42293270"],"award-info":[{"award-number":["42293270"]}]},{"name":"Major Program of the National Natural Science Foundation of China","award":["42071374"],"award-info":[{"award-number":["42071374"]}]},{"name":"Major Program of the National Natural Science Foundation of China","award":["KPI001"],"award-info":[{"award-number":["KPI001"]}]},{"name":"National Program of National Natural Science Foundation of China","award":["42293270"],"award-info":[{"award-number":["42293270"]}]},{"name":"National Program of National Natural Science Foundation of China","award":["42071374"],"award-info":[{"award-number":["42071374"]}]},{"name":"National Program of National Natural Science Foundation of China","award":["KPI001"],"award-info":[{"award-number":["KPI001"]}]},{"name":"Key Project of Innovation LREIS","award":["42293270"],"award-info":[{"award-number":["42293270"]}]},{"name":"Key Project of Innovation LREIS","award":["42071374"],"award-info":[{"award-number":["42071374"]}]},{"name":"Key Project of Innovation LREIS","award":["KPI001"],"award-info":[{"award-number":["KPI001"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>High accuracy and a high spatiotemporal resolution of precipitation are essential for the hydrological, ecological, and environmental fields. However, the existing daily gridded precipitation datasets, such as remote sensing products, are limited both by the coarse resolution and the low accuracy. Despite considerable efforts having been invested in downscaling or merging, a method of coupled and simultaneously downscaling and merging multiple datasets is currently lacking, which limits the wide application of individual popular satellite precipitation products. For the first time, in this study, we propose a simple coupled merging and downscaling (CMD) method for simultaneously obtaining multiple high-resolution and high-accuracy daily precipitation datasets. A pixel-repeated decomposition method was first proposed, and the random forest (RF) method was then applied to merge multiple daily precipitation datasets. The individual downscaled dataset was obtained by multiplying the result of merging by an explanatory rate obtained by RF. The results showed that the CMD method exhibited significantly better performance compared with the original datasets, with the mean absolute error (MAE) improving by up to 50%, the majority of the values of bias ranging between \u22121 mm and 1 mm, and the majority of the Kling\u2013Gupta efficiency (KGE) values being greater than 0.7. CMD was more accurate than the widely used dataset, Multi-Source Weighted-Ensemble Precipitation (MSWEP), with a 43% reduction in the MAE and a 245% improvement in the KGE. In addition, the long-term estimation suggested that the proposed method exhibits stable good performance over time.<\/jats:p>","DOI":"10.3390\/rs15184377","type":"journal-article","created":{"date-parts":[[2023,9,6]],"date-time":"2023-09-06T10:23:42Z","timestamp":1693995822000},"page":"4377","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["A Simple Method of Coupled Merging and Downscaling for Multi-Source Daily Precipitation Data"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4434-1726","authenticated-orcid":false,"given":"Na","family":"Zhao","sequence":"first","affiliation":[{"name":"State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China"},{"name":"College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100101, China"},{"name":"Jiangsu Center for Collaborative Innovation in Geographic Information Resource Development and Application, Nanjing 210023, China"}]},{"given":"Kainan","family":"Chen","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China"},{"name":"First Institute of Oceanography, Ministry of Natural Resources, Qingdao 266061, China"}]}],"member":"1968","published-online":{"date-parts":[[2023,9,6]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"663","DOI":"10.5194\/cp-16-663-2020","article-title":"Statistical reconstruction of daily precipitation and temperature fields in Switzerland back to 1864","volume":"16","author":"Pfister","year":"2020","journal-title":"Clim. 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