Long-lead forecasts of El Niño events are lacking despite their enormous societal and economic impacts. These climatic events lead to floods and droughts in many tropical regions, and damage agriculture and the economy in poor countries. Due to their impact on local climate, they can also affect human health by increasing the risk of vector-borne diseases, such as dengue fever. Physical processes at the origin of this complex coupled ocean-atmosphere phenomenon are just beginning to be better understood, with subsurface processes and stored heat as two of the main driving forces leading to the development of El Niño in a quasi-periodic manner. Taking advantage of this new knowledge, a statistical dynamic components model, using a state space approach and predictors relevant to the El Niño evolution, was specifically tailored to forecast warm events at lead times of about 2 years (well beyond the traditional spring barrier limit in El Niño predictability). This forecasting scheme provides skilful information on the amplitude of El Niño events, their duration, and the peak time of the sea surface temperature anomalies at a sufficient lead time as to efficiently serve preventive public health actions. The long-lead El Niño predictions were coupled to a statistical dengue model to estimate dengue cases during the 1998 and the 2010 epidemics in El Oro Province in Ecuador, where dengue is hyper-endemic. The dengue model correctly estimated these two largest dengue epidemics even at a 2-year simulation lead time. Thus, information is successfully passed from the El Niño forecast domain to the dengue estimation domain, and the long-lead El Niño predictions are shown to potentially anticipate the magnitude of dengue epidemics in the peak season. The results validate the sensitivity of large dengue epidemics in the region to the El Niño forecasts within the proposed model coupling set-up and imply a potential for increasing lead-time in dengue prediction. This coupled model framework and exploratory analysis, based on El Niño predictions, could be easily extended to other similarly transmitted diseases in tropical and subtropical countries, which are directly and severely affected by the large-scale temperature and precipitation teleconnections occurring before, during and after El Niño events.