This study explored the impact of weather variability on the transmission of dengue fever in Nakhon Si Thammarat, Thailand. Data on monthly-notified cases of dengue fever, over the period of January 1981 - June 2012 were collected from the Bureau of Epidemiology, Department of Disease Control, Ministry of Public Health. Weather data over the same period were obtained from the Thai Meteorological Department. Spearman correlation analysis and time-series adjusted Poisson regression analysis were performed to quantify the relationship between weather and the number of dengue cases. The results showed that maximum and minimum temperatures at a lag of zero months, the amount of rainfall, and relative humidity at a lag of two months were significant predictors of dengue incidence in Nakhon Si Thammarat. The time series Poisson regression model demonstrated goodness-of-fit with a correlation between observed and predicted number of dengue incidence rate of 91.82%. This model could be used to optimise dengue prevention by predicting trends in dengue incidence. Accurate predictions, for even a few months, provide an invaluable opportunity to mount a vector control intervention or to prepare for hospital demand in the community.