Rift Valley fever (RVF) is a zoonotic mosquito-borne disease whose outbreaks are associated with periodic heavy rainfall episodes and flooding. Baringo County is found in the arid and semi-arid lands of Kenya, that has experienced increased frequency of extreme rainfall events and RVF outbreaks. It is inhabited by resource-poor pastoralists who are also affected by other climate-sensitive vector-borne diseases and who have previously incurred massive economic losses due to RVF outbreaks. The overall objective of this study was to investigate the effect of climate change on RVF outbreaks. Specifically, the study set out to model the spatial distribution of RVF vectors, predict the effect of climate change on the future spatial distribution of RVF vectors, and determine the climatic factors behind the 2006-2007 RVF outbreak in Baringo County. The current and future climate change-driven spatial distribution of RVF vectors was determined by modelling vector presence data against current and forecasted climatic and environmental variables using the Maximum Entropy (MaxEnt) ecological niche modelling algorithm. Binomial regression models were used to test the influence of climatic factors on the 2006-2007 RVF outbreak. Four species of RVF vectors namely Culex quinquefasciatus, Culex univitattus, Mansonia africana, and Mansonia uniformis were identified. Their suitable habitats were found to be the lowlands between Lake Baringo and Lake Bogoria, and the riverine habitats along Kerio River (p < 0.001). The spatial distribution of RVF vectors was found to be determined by soil types (p < 0.001), precipitation (p < 0.001), and isothermality (p <0.001). The study predicted that climate change will cause an expansion in the ecological range of Cx. quinquefasciatus and M. africana into new habitats (p < 0.001). The most important climatic variable associated with the 2006-2007 RVF outbreak was the temperature (p=0.048). Pastoralists inhabit the suitable habitats for RVF vectors, implying a potent risk of RVF transmission. Likewise, the predicted expansion in the ecological range of RVF vectors will expose new populations to the risk of RVF infection. Interventions against RVF outbreaks should focus on populations inhabiting geographic locations coinciding to the current and predicted vector niches. In addition to heavy rainfall, fluctuations in temperature should be monitored as a predictor to RVF outbreaks. These findings are useful to government officials and the local community in Baringo County to plan interventions against future RVF outbreaks.