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Operational physics\u2010based numerical models are run to predict S2S sea surface temperature and corresponding climate indices, such as El Ni\u00f1o\u2010Southern Oscillation. However, these models are hampered by computational inefficiencies, rapidly dissipating ocean\u2010atmosphere initial conditions, and significant uncertainty and biases. Here, we introduce a novel spatiotemporal deep learning neural network to model the nonlinear and complex coupled atmosphere\u2010ocean weather systems. This model, named as CAS\u2010Canglong, incorporates climatic and temporal features and employs a self\u2010attention mechanism to enhance the prediction of global S2S sea surface temperature pattern. Compared to the physics\u2010based models, it shows higher computational efficiency and more predictive capability, improving one to three months sea surface temperature predictive skill by 55.6 \u00b1 14.1% to 63.4 \u00b1 15.5% in six ocean regions with dominant influence on S2S variability over land. This achievement underscores the huge potential of deep learning for largely improving forecasting skills at the S2S timescales over land.<\/jats:p>","DOI":"10.1002\/advs.78271","type":"journal-article","created":{"date-parts":[[2026,10,16]],"date-time":"2026-10-16T03:06:50Z","timestamp":1792120010000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Improved Skills for Predicting Sub\u2010Seasonal to Seasonal Global Sea Surface Temperature"],"prefix":"10.1002","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4642-4701","authenticated-orcid":false,"given":"Longhao","family":"Wang","sequence":"first","affiliation":[{"name":"Key Laboratory of Water Cycle and Related Land Surface Processes Institute of Geographic Sciences and Natural Resources Research Chinese Academy of Sciences  Beijing China"},{"name":"College of Resources and Environment University of Chinese Academy of Sciences  Beijing China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xuanze","family":"Zhang","sequence":"additional","affiliation":[{"name":"Key Laboratory of Water Cycle and Related Land Surface Processes Institute of Geographic Sciences and Natural Resources Research Chinese Academy of Sciences  Beijing China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3221-9467","authenticated-orcid":false,"given":"Ruby L.","family":"Leung","sequence":"additional","affiliation":[{"name":"Pacific Northwest National Laboratory  Richland Washington USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8020-8773","authenticated-orcid":false,"given":"Francis H. S.","family":"Chiew","sequence":"additional","affiliation":[{"name":"CSIRO Environment, Black Mountain  Canberra ACT Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4689-8357","authenticated-orcid":false,"given":"Amir","family":"AghaKouchak","sequence":"additional","affiliation":[{"name":"Center for Hydrometeorology and Remote Sensing University of California  Irvine California USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4214-3507","authenticated-orcid":false,"given":"Kairan","family":"Ying","sequence":"additional","affiliation":[{"name":"National Institute of Natural Hazards Ministry of Emergency Management of China  Beijing China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3562-2323","authenticated-orcid":false,"given":"Yongqiang","family":"Zhang","sequence":"additional","affiliation":[{"name":"Key Laboratory of Water 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