Study rationale Climate change is likely to affect timing, intensity, duration, frequency and distribution of outbreaks of vector-borne diseases. Early warning of a build-up of climatic conditions conducive to outbreaks would be valuable for directing decision-making about disease preventive actions. This study tested if climate and weather factors can predict epidemics of the major vector-borne disease in Australia–Ross River virus disease–with sufficient timeliness and accuracy to be of use as a tool for public health response. Methods Weather and climate data from a region in south-west Western Australia were matched with Ross River virus disease data and demographic data for the period 1991 to 1999. We used logistic regression models to estimate the probability of the occurrence of an epidemic in a Statistical Local Area in a year. Two models were developed–an “early warning model” and a “late warning model”. We tested the additional predictive skill provided by mosquito density data, above that of climate/weather factors. Results A precondition for an epidemic, relating to host-virus dynamics, was low tide heights in the pre-epidemic year. High tides and rainfall in late spring, accompanied by high early spring temperatures, were predictors of epidemics in the south-west. Epidemics were predicted with reasonable precision using weather/climate factors alone (sensitivity = 63%, specificity = 93%). The addition of mosquito density information to the model appeared to increase sensitivity dramatically (90%) and specificity remained very high (98%). Conclusions This case study provides a number of lessons for public health and the adaptation of communities. It demonstrates the value of early warning as a planning tool for climate-related health risks. It supports previous observations that climate/weather factors are important drivers of epidemics of vector-borne diseases, and that additional information about vector dynamics-when available-can provide a substantial improvement in prediction. *This work was partly funded by the Electric Power Research Institute, Palo Alto.