{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,12]],"date-time":"2025-10-12T02:10:22Z","timestamp":1760235022560,"version":"build-2065373602"},"reference-count":57,"publisher":"MDPI AG","issue":"14","license":[{"start":{"date-parts":[[2021,7,8]],"date-time":"2021-07-08T00:00:00Z","timestamp":1625702400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["42071374, 41930647"],"award-info":[{"award-number":["42071374, 41930647"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Program of Frontier Sciences of Chinese Academy of Sciences","award":["ZDBS-LY-DQC005"],"award-info":[{"award-number":["ZDBS-LY-DQC005"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Obtaining high-quality precipitation datasets with a fine spatial resolution is of great importance for a variety of hydrological, meteorological and environmental applications. Satellite-based remote sensing can measure precipitation in large areas but suffers from inherent bias and relatively coarse resolutions. Based on the high accuracy surface modeling method (HASM), this study proposed a new downscaling method, the high accuracy surface modeling-based downscaling method (HASMD), to derive high-quality monthly precipitation estimates at a spatial resolution of 0.01\u00b0 by downscaling the Integrated Multi-satellitE Retrievals for Global Precipitation Measurement (IMERG) precipitation estimates in China. A scale transformation equation was introduced in HASMD, and the initial value was set by including the explanatory variables related to precipitation. The performance of HASMD was evaluated by comparing the results yielded by HASM and the combined method of HASM, Kriging, IDW and the geographical weighted regression (GWR) method (GWR-HASM, GWR-Kriging, GWR-IDW). Analysis results indicated that HASMD performed better than the other four methods. High agreement was achieved for HASMD, with bias values ranging from 0.07 to 0.29, root mean square error (RMSE) values ranging from 9.53 mm to 47.03 mm, and R2 values ranging from 0.75 to 0.96. Compared with the original IMERG precipitation products, the downscaling accuracy with HASMD improved up to 47%, 47%, and 14% according to bias, RMSE and R2, respectively. HASMD was able to capture the spatial variation in monthly precipitation in a vast region, and it might be potentially applicable for enhancing the spatial resolution and accuracy of remotely sensed precipitation data and facilitating their application at large scales.<\/jats:p>","DOI":"10.3390\/rs13142693","type":"journal-article","created":{"date-parts":[[2021,7,8]],"date-time":"2021-07-08T10:42:17Z","timestamp":1625740937000},"page":"2693","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["A New HASM-Based Downscaling Method for High-Resolution Precipitation Estimates"],"prefix":"10.3390","volume":"13","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":"Yimeng","family":"Jiao","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":"College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100101, China"}]}],"member":"1968","published-online":{"date-parts":[[2021,7,8]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"861","DOI":"10.1038\/ngeo2846","article-title":"Atmospheric science: Energy and precipitation","volume":"9","author":"Donohoe","year":"2016","journal-title":"Nat. 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