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Adv."],"published-print":{"date-parts":[[2026,10,9]]},"abstract":"<jats:p>Artificial intelligence (AI) models have transformed weather forecasting, but their skill for unprecedented weather extremes is unclear. Here, we analyze GraphCast, AIFS, and FuXi forecasts of the unprecedented 2024 Dubai storm, which had twice the training set\u2019s highest rainfall in that region. GraphCast and AIFS accurately forecast this event up to 8 days ahead. FuXi forecasts the event but underestimates the rainfall. Fine-tuning and receptive field analyses suggest that these models\u2019 success stems from \u201ctranslocation\u201d: learning from comparable\/stronger dynamically similar events in other regions during training. Evidence of \u201cextrapolation\u201d (learning from weaker events) is not found. Even events within the global distribution\u2019s tail are poorly forecasted, which is not only due to data imbalance (generalization error) but also spectral bias (optimization error). These findings demonstrate the potential of AI models to forecast \u201cregional\u201d gray swans and the opportunity to improve them through understanding the mechanisms behind their successes\/limitations.<\/jats:p>","DOI":"10.1126\/sciadv.ady9406","type":"journal-article","created":{"date-parts":[[2026,10,7]],"date-time":"2026-10-07T17:58:33Z","timestamp":1791395913000},"update-policy":"https:\/\/doi.org\/10.34133\/aaas_crossmark","source":"Crossref","is-referenced-by-count":0,"title":["Predicting regional gray swans via translocation: AI weather models and Dubai\u2019s unprecedented 2024 rainfall"],"prefix":"10.1126","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5190-4681","authenticated-orcid":true,"given":"Y.","family":"Sun","sequence":"first","affiliation":[{"name":"Department of the Geophysical Sciences, University of Chicago, Chicago, IL 60637, USA."},{"name":"State Key Laboratory of Severe Weather Meteorological Science and Technology, Nanjing University, Nanjing 210023, China."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9425-8085","authenticated-orcid":true,"given":"Pedram","family":"Hassanzadeh","sequence":"additional","affiliation":[{"name":"Department of the Geophysical Sciences, University of Chicago, Chicago, IL 60637, USA."},{"name":"Committee on Computational and Applied Mathematics, University of Chicago, Chicago, IL 60637, USA."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0551-6810","authenticated-orcid":true,"given":"Tiffany","family":"Shaw","sequence":"additional","affiliation":[{"name":"Department of the Geophysical Sciences, University of Chicago, Chicago, IL 60637, USA."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3964-6223","authenticated-orcid":true,"given":"Hamid A.","family":"Pahlavan","sequence":"additional","affiliation":[{"name":"Department of the Geophysical Sciences, University of Chicago, Chicago, IL 60637, USA."},{"name":"NorthWest Research Associates, Boulder, CO 80301, USA."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0000-6029-4168","authenticated-orcid":true,"given":"Adam","family":"Marchakitus","sequence":"additional","affiliation":[{"name":"Department of the Geophysical Sciences, University of Chicago, Chicago, IL 60637, USA."}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"221","reference":[{"key":"e_1_3_2_2_2","unstructured":"J. Pathak S. Subramanian P. Harrington S. Raja A. Chattopadhyay M. Mardani T. Kurth D. Hall Z. Li K. Azizzadenesheli P. Hassanzadeh K. Kashinath A. Anandkumar Fourcastnet: A global data-driven high-resolution weather model using adaptive fourier neural operators. arXiv:2202.11214 [physics.ao-ph] (2022)."},{"key":"e_1_3_2_3_2","doi-asserted-by":"publisher","DOI":"10.1038\/s41586-023-06185-3"},{"key":"e_1_3_2_4_2","doi-asserted-by":"publisher","DOI":"10.1126\/science.adi2336"},{"key":"e_1_3_2_5_2","doi-asserted-by":"publisher","DOI":"10.1038\/s41612-023-00512-1"},{"key":"e_1_3_2_6_2","unstructured":"S. Lang M. Alexe M. Chantry J. Dramsch F. Pinault B. Raoult M. C. A. Clare C. Lessig M. Maier-Gerber L. Magnusson Z. Ben Bouall\u00e8gue A. Prieto Nemesio P. D. Dueben A. Brown F. Pappenberger F. Rabier AIFS-ECMWF\u2019s data-driven forecasting system. arXiv:2406.01465 [physics.ao-ph] (2024)."},{"key":"e_1_3_2_7_2","doi-asserted-by":"publisher","DOI":"10.1038\/s41586-024-07744-y"},{"key":"e_1_3_2_8_2","doi-asserted-by":"publisher","DOI":"10.1038\/s41586-024-08252-9"},{"key":"e_1_3_2_9_2","unstructured":"K. Chen T. Han J. Gong L. Bai F. Ling J.-J. Luo X. Chen L. Ma T. Zhang R. Su Y. Ci B. Li X. Yang W. 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Berner CAMulator: Fast emulation of the community atmosphere model. arXiv:2504.06007 [physics.ao-ph] (2025)."},{"key":"e_1_3_2_70_2","doi-asserted-by":"crossref","unstructured":"F. Z. Bassine T. E. Epule S. Eddamiri \u201cThe role of artificial intelligence in understanding rainfall extremes in Africa \u201d in Climate Change and Rainfall Extremes in Africa (Elsevier 2025) pp. 71\u201390.","DOI":"10.1016\/B978-0-443-28867-8.00004-6"},{"key":"e_1_3_2_71_2","doi-asserted-by":"publisher","DOI":"10.1029\/2025GL115432"},{"key":"e_1_3_2_72_2","unstructured":"P. Laloyaux M. Alexe E. Boucher P. Lean E. Pinnington S. Lang T. Necker A. McNally Using data assimilation tools to dissect GraphDOP. arXiv:2510.27388 [physics.ao-ph] (2025)."},{"key":"e_1_3_2_73_2","unstructured":"R. Baiman E. A. Barnes A. 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Gerber Overcoming set imbalance in data driven parameterization: A case study of gravity wave momentum transport. arXiv:2402.18030 [physics.ao-ph] (2024)."},{"key":"e_1_3_2_82_2","doi-asserted-by":"publisher","DOI":"10.1073\/pnas.1712645115"},{"key":"e_1_3_2_83_2","doi-asserted-by":"publisher","DOI":"10.1029\/2020GL091197"},{"key":"e_1_3_2_84_2","doi-asserted-by":"publisher","DOI":"10.1063\/1.5081461"},{"key":"e_1_3_2_85_2","doi-asserted-by":"publisher","DOI":"10.1029\/2024MS004264"},{"key":"e_1_3_2_86_2","doi-asserted-by":"publisher","DOI":"10.1038\/s41467-023-40112-4"},{"key":"e_1_3_2_87_2","doi-asserted-by":"publisher","DOI":"10.1002\/qj.3803"},{"key":"e_1_3_2_88_2","first-page":"2581","article-title":"Sensitivity of Northern Hemisphere cyclone detection and tracking results to fine spatial and temporal resolution using ERA5","volume":"149","author":"Crawford A. D.","year":"2021","unstructured":"A. D. Crawford, E. A. Schreiber, N. Sommer, M. C. Serreze, J. C. Stroeve, D. G. 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