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In recent decades, the prediction skill for ENSO has improved significantly; however, accurate forecasting at a lead time of more than six months remains challenging. By using a machine learning method called eXtreme Gradient Boosting (XGBoost), we corrected the ENSO predicted results from the First Institute of Oceanography Climate Prediction System version 2.0 (FIO\u2212CPS v2.0) based on the satellite remote sensing sea surface temperature data, and then developed a dynamic and statistical hybrid prediction model, named FIO\u2212CPS\u2212HY. The latest 15 years (2007\u20132021) of independent testing results showed that the average anomaly correlation coefficient (ACC) and root mean square error (RMSE) of the Ni\u00f1o3.4 index from FIO\u2212CPS v2.0 to FIO\u2212CPS\u2212HY for 7\u2212 to 13\u2212month lead times could be increased by 57.80% (from 0.40 to 0.63) and reduced by 24.79% (from 0.86 \u00b0C to 0.65 \u00b0C), respectively. The real\u2212time predictions from FIO\u2212CPS\u2212HY indicated that the sea surface state of the Ni\u00f1o3.4 area would likely be in neutral conditions in 2023. Although FIO\u2212CPS\u2212HY still has some biases in real\u2212time prediction, this study provides possible ideas and methods to enhance short\u2212term climate prediction ability and shows the potential of integration between machine learning and numerical models in climate research and applications.<\/jats:p>","DOI":"10.3390\/rs15071728","type":"journal-article","created":{"date-parts":[[2023,3,24]],"date-time":"2023-03-24T02:34:54Z","timestamp":1679625294000},"page":"1728","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["A Hybrid ENSO Prediction System Based on the FIO\u2212CPS and XGBoost Algorithm"],"prefix":"10.3390","volume":"15","author":[{"given":"Zhiyuan","family":"Kuang","sequence":"first","affiliation":[{"name":"First Institute of Oceanography, and Key Laboratory of Marine Science and Numerical Modeling, Ministry of Natural Resources, Qingdao 266061, China"},{"name":"Shandong Key Laboratory of Marine Science and Numerical Modeling, Qingdao 266061, China"}]},{"given":"Yajuan","family":"Song","sequence":"additional","affiliation":[{"name":"First Institute of Oceanography, and Key Laboratory of Marine Science and Numerical Modeling, Ministry of Natural Resources, Qingdao 266061, China"},{"name":"Shandong Key Laboratory of Marine Science and Numerical Modeling, Qingdao 266061, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7806-6718","authenticated-orcid":false,"given":"Jie","family":"Wu","sequence":"additional","affiliation":[{"name":"China Meteorological Administration Key Laboratory for Climate Prediction Studies, National Climate Center, Beijing 100081, China"}]},{"given":"Qiuying","family":"Fu","sequence":"additional","affiliation":[{"name":"First Institute of Oceanography, and Key Laboratory of Marine Science and Numerical Modeling, Ministry of Natural Resources, Qingdao 266061, China"},{"name":"Shandong Key Laboratory of Marine Science and Numerical Modeling, Qingdao 266061, China"}]},{"given":"Qi","family":"Shu","sequence":"additional","affiliation":[{"name":"First Institute of Oceanography, and Key Laboratory of Marine Science and Numerical Modeling, Ministry of Natural Resources, Qingdao 266061, China"},{"name":"Shandong Key Laboratory of Marine Science and Numerical Modeling, Qingdao 266061, China"}]},{"given":"Fangli","family":"Qiao","sequence":"additional","affiliation":[{"name":"First Institute of Oceanography, and Key Laboratory of Marine Science and Numerical Modeling, Ministry of Natural Resources, Qingdao 266061, China"},{"name":"Shandong Key Laboratory of Marine Science and Numerical Modeling, Qingdao 266061, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8098-5529","authenticated-orcid":false,"given":"Zhenya","family":"Song","sequence":"additional","affiliation":[{"name":"First Institute of Oceanography, and Key Laboratory of Marine Science and Numerical Modeling, Ministry of Natural Resources, Qingdao 266061, China"},{"name":"Shandong Key Laboratory of Marine Science and Numerical Modeling, Qingdao 266061, China"}]}],"member":"1968","published-online":{"date-parts":[[2023,3,23]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"163","DOI":"10.1175\/1520-0493(1969)097<0163:ATFTEP>2.3.CO;2","article-title":"Atmospheric teleconnections from the equatorial Pacific","volume":"97","author":"Bjerknes","year":"1969","journal-title":"Mon. 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