Purpose: Environmental change impacts the transmission and spread of vector-borne diseases. Significant associations between climate factors and vector-borne diseases have enabled predictive models to be developed that can be used in early warning systems to forecast and anticipate disease epidemics. Malaria is one such vector-borne disease, with a global distribution and significant health burden that is highly sensitive to climatic factors. Additionally, malaria is experiencing a resurgence in transmission, threatening elimination efforts. Malaria is also sensitive to non-climatic factors, such as land-use change, political instability, insecticide resistance and the effectiveness of public health control interventions. A greater understanding of the relative role of climate and non-climate factors in driving malaria transmission is needed to develop predictive models, particularly under future global change scenarios. Methods & Materials: In this study, a 20-year dataset of monthly malaria cases was used to develop a spatiotemporal Bayesian modelling framework for evaluating the relative influences of climatic and non-climatic drivers of malaria incidence in El Oro province in southern Ecuador, an area historically endemic for Plasmodium vivax and P. falciparum. Between the 1980s and early 2000s, El Oro experienced a surge in malaria transmission and through an effective binational collaboration became effectively malaria free in 2011. However, there is still a high risk of re-emergence of malaria in the region. Results: The mixed model with the best fit included the climatic variables minimum temperature and precipitation. The addition of non-climate factors, including political instability and the addition of malaria intervention activities in El Oro were also included in this model. By interacting the climate covariates with malaria intervention periods, we found the association between climate and malaria risk changed significantly following the interventions. Conclusion: Distinct variation in spatially explicit random effects was found, which suggest that other environmental factors, such as land use change or area-specific intervention efforts should be explored to understand this variation. This study demonstrates that both environmental, socio-economic and political factors should be considered when developing predictive disease models, to understand the potential environmental suitability for re-emergence in the region, given a lapse in control efforts or periods of civil unrest.