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Hydroclimatic variables, particularly local temperature, precipitation, relative humidity, and large\u2010scale climate teleconnections, can influence the prevalence of dengue by impacting vector population development, viral replication, and human\u2010mosquito interactions. Leveraging predictions of these variables at lead times of weeks to months can facilitate early warning system preparatory actions such as allocating funding, acquisition and preparation of medical supplies, or implementation of vector control strategies. We develop hydroclimate\u2010based statistical forecast models for dengue virus (DENV) at 1\u2010, 3\u2010, and 6\u2010 month lead times for four cities across Colombia (Cali, C\u00facuta, Medell\u00edn, and Leticia) and compare with standard autoregressive models conditioned on dengue case counts. Our results indicate that (a) hydroclimate\u2010based models are particularly skillful at 3\u2010 and 6\u2010 month lead times when autoregressive models often fail, (b) sea surface temperatures are the most skillful predictor at 3\u2010 and 6\u2010 month leads and (c) application of hydroclimate models are most beneficial when average DENV incidence is low, autoregressive relationships are weak, but outbreaks may still occur.<\/jats:p>","DOI":"10.1029\/2024gh001325","type":"journal-article","created":{"date-parts":[[2025,9,1]],"date-time":"2025-09-01T14:09:58Z","timestamp":1756735798000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Forecasting Dengue: Evaluating the Role of Hydroclimate Information in Subseasonal to Seasonal Prediction"],"prefix":"10.1029","volume":"9","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2332-0704","authenticated-orcid":false,"given":"Maxwell R. 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