{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,1]],"date-time":"2026-04-01T07:25:52Z","timestamp":1775028352734,"version":"3.50.1"},"reference-count":45,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2021,5,23]],"date-time":"2021-05-23T00:00:00Z","timestamp":1621728000000},"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":["41575070"],"award-info":[{"award-number":["41575070"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["2018YFC1507703"],"award-info":[{"award-number":["2018YFC1507703"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>The present study investigates the skills of CORDEX-CORE precipitation outputs in simulating Africa\u2019s key seasonal climate features, emphasizing the added value (AV) of the dynamical downscaling approach from which they were derived. The results indicate the models\u2019 good skills in capturing African rainfall patterns and dynamics at satellite-based observation resolutions, with up to 65.17% significant positive AV spatial coverage for the CCLM5 model and up to 55.47% significant positive AV spatial coverage for the REMO model. Unavoidable biases are however present in rainfall-abundant areas and are reflected in the AV results, but vary based on the season, the sub-area, and the Global Climate Model\u2013Regional Climate Models (GCM-RCM) combination considered. The RCMs\u2019 ensemble mean generally performs better than individual GCM\u2013RCM simulations. A further analysis of the GCM\u2013RCM model chain indicates a strong influence of the dynamical downscaling approach on the driving GCMs. However, exceptions are found in some seasons for specific RCMs\u2019 outputs, where GCMs are influential. The findings also revealed that observational uncertainties can influence AV and contribute to a 6 to 34% difference in significant positive AV spatial coverage results. An analysis of these results suggests that the AV by CORDEX-CORE simulations over Africa depend on how well the GCM physics are integrated to those of the RCMs and how these features are accommodated in the high-resolution setting of the downscaling experiments. The deficiencies of the CORDEX-CORE simulations could be related to how well key processes are represented within the RCM models. For Africa, these results show that CORDEX-CORE products could be adequate for a wide range of high-resolution precipitation data applications.<\/jats:p>","DOI":"10.3390\/rs13112058","type":"journal-article","created":{"date-parts":[[2021,5,24]],"date-time":"2021-05-24T00:01:20Z","timestamp":1621814480000},"page":"2058","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":25,"title":["Assessing Past Climate Biases and the Added Value of CORDEX-CORE Precipitation Simulations over Africa"],"prefix":"10.3390","volume":"13","author":[{"given":"Gnim Tchalim","family":"Gnitou","sequence":"first","affiliation":[{"name":"Binjiang College, Nanjing University of Information Science & Technology, Wuxi 214105, China"}]},{"given":"Guirong","family":"Tan","sequence":"additional","affiliation":[{"name":"Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters\/Key Laboratory of Meteorological, Disaster, Ministry of Education, Nanjing University of Information Science and Technology, Nanjing 210044, China"}]},{"given":"Ruoyun","family":"Niu","sequence":"additional","affiliation":[{"name":"National Meteorological Centre, China Meteorological Administration, Beijing 100081, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6636-9554","authenticated-orcid":false,"given":"Isaac Kwesi","family":"Nooni","sequence":"additional","affiliation":[{"name":"Binjiang College, Nanjing University of Information Science & Technology, Wuxi 214105, China"},{"name":"School of Geographical Sciences, Nanjing University of Information Science & Technology, Nanjing 210044, China"}]}],"member":"1968","published-online":{"date-parts":[[2021,5,23]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"5696","DOI":"10.1029\/2018JD030094","article-title":"Thirty Years of Regional Climate Modeling: Where Are We and Where Are We Going next?","volume":"124","author":"Giorgi","year":"2019","journal-title":"J. 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