BACKGROUND & OBJECTIVES: Transmission of dengue virus depends on the presence of Aedes mosquito. Mosquito generation and development is known to be influenced by the climate. This study was carried out to examine whether the climatic factors data can be used to predict yearly dengue cases of Dhaka city, Bangladesh. METHODS: Monthly reported dengue cases and climate data for the years 2000-2008 were obtained from the Directorate General of Health Services (DGHS) and Meteorological Department of Dhaka, Bangladesh, respectively. Data for the period 2000 to 2007 were used for development of a model through multiple linear regressions. Retrospective validation of the model was done with 2001, 2003, 2005 and 2008 data. Log transformation of the dependent variable was done to normalize data for linear regression. Average monthly humidity, rainfall, minimum and maximum temperature were used as independent variables and number of dengue cases reported monthly was used as dependent variable. Accuracy of the model for predicting outbreak was assessed through receiver operative characteristics (ROC) curve. RESULTS: Climatic factors, i.e. rainfall, maximum temperature and relative humidity were significantly correlated with monthly reported dengue cases. The model incorporating climatic data of two-lag month explained 61 per cent of variation in number of reported dengue cases and this model was found to predict dengue outbreak (>/Euro Surveillance (Bulletin Europeen Sur Les Maladies Transmissibles; European Communicable Disease Bulletin) 200 cases) with considerable accuracy [area under ROC curve Euro Surveillance (Bulletin Europeen Sur Les Maladies Transmissibles; European Communicable Disease Bulletin) 0.89, 95%CI Euro Surveillance (Bulletin Europeen Sur Les Maladies Transmissibles; European Communicable Disease Bulletin) (0.89-0.98)]. INTERPRETATION & CONCLUSIONS: Our results showed that the climate had a major effect on the occurrence of dengue infection in Dhaka city. Though the prediction model had some limitations in predicting the monthly number of dengue cases, it could forecast possible outbreak two months in advance with considerable accuracy.