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Here, we leverage an underappreciated factor in ENSO forecasting: the substantial variation in forecast skill based on the ENSO phase at the time a forecast is issued. Our analysis of transitions between ENSO phases in a dynamical hindcast dataset demonstrates that this variation markedly exceeds intermodel differences in predictive capability. Forecasts initialized during La Ni\u00f1a conditions perform best, with three models correctly predicting transitions to El Ni\u00f1o 1 year in advance over 70% of the time. We present our findings in a simple framework that allows decision-makers to assess the likelihood of a forecast being correct on the basis of the current ENSO state. Applied effectively, these findings could improve long-lead ENSO forecasting strategies, enhancing global risk management.<\/jats:p>","DOI":"10.1126\/sciadv.aeg8654","type":"journal-article","created":{"date-parts":[[2026,10,23]],"date-time":"2026-10-23T17:58:42Z","timestamp":1792778322000},"update-policy":"https:\/\/doi.org\/10.34133\/aaas_crossmark","source":"Crossref","is-referenced-by-count":0,"title":["Using initial states to identify successful El Ni\u00f1o\u2013Southern Oscillation forecasts 1 year in advance"],"prefix":"10.1126","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9538-2042","authenticated-orcid":true,"given":"Nandini","family":"Ramesh","sequence":"first","affiliation":[{"name":"Natural Systems Modelling Group, Technology, Commonwealth Scientific and Industrial Research Organisation (CSIRO), Sydney 2145, Australia."},{"name":"School of Mathematics and Statistics, University of New South Wales, Sydney 2052, 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