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As a common vector-borne disease, dengue fever remains challenging to predict due to large variations in epidemic size across seasons driven by a number of factors including population susceptibility, mosquito density, meteorological conditions, geographical factors, and human mobility. An ensemble forecast system for dengue fever is first proposed that addresses the difficulty of predicting outbreaks with drastically different scales. The ensemble forecast system based on a susceptible-infected-recovered (SIR) type of compartmental model coupled with a data assimilation method called the ensemble adjusted Kalman filter (EAKF) is constructed to generate real-time forecasts of dengue fever spread dynamics. The model was informed by meteorological and mosquito density information to depict the transmission of dengue virus among human and mosquito populations, and generate predictions. To account for the dramatic variations of outbreak size in different seasons, the effective population size parameter that is sequentially updated to adjust the predicted outbreak scale is introduced into the model. Before optimizing the transmission model, we update the effective population size using the most recent observations and historical records so that the predicted outbreak size is dynamically adjusted. In the retrospective forecast of dengue outbreaks in Guangzhou, China during the 2011–2017 seasons, the proposed forecast model generates accurate projections of peak timing, peak intensity, and total incidence, outperforming a generalized additive model approach. The ensemble forecast system can be operated in real-time and inform control planning to reduce the burden of dengue fever. Author summary: Dengue fever is one of the key neglected tropical diseases (NTDs) and threatens the health of billions of people worldwide. Nevertheless, accurate forecasts of dengue outbreaks remain difficult: the intensity of dengue fever outbreaks varies remarkably among different seasons and there are generally no cases or only sporadic cases of dengue fever outside the outbreak season. From the perspective of data distribution characteristics, the time series of dengue incidence are following zero-inflated distributions. Therefore, generating model forecasts using the type of zero-inflated data is usually beyond the predictive power of general discrete distributions such as Poisson distributions. These characteristics impose great challenges in accurate dengue predictions. We proposed to establish a combined forecast system based on susceptible-infected-recovered (SIR) model coupled with ensemble adjusted Kalman filter (EAKF) algorithm for depicting dengue transmission dynamics, thus accounting for the dramatic variations of outbreak size in different seasons. This approach allows for early warning of epidemics and outbreaks of dengue, and can inform control planning to reduce the burden of the disease. [ABSTRACT FROM AUTHOR]