The World Health Organization (WHO) estimates that malaria causes 800,000 deaths worldwide every year . The prevalence of malaria depends on interactions between mosquitoes and humans, as well as environmental parameters such as temperature and precipitation. Accordingly, malaria outbreaks have been observed to coincide with seasonal environmental changes [1]. The usefulness of computational outbreak models has been demonstrated in the public health sector, where such tools have been used for emergency preparedness and response planning . Based on the principles of classic mathematical epidemiology models and the Global Stochastic Contact Model (GSCM) we have developed a coupled disease transmission simulator for the purpose of investigating malaria disease dynamics. We refer to this model as the GSCM for malaria. Additional features aid in simulating the effects of varying temperature on the sporogonic cycle of Plasmodium falciparum, the parasite that causes malaria.We investigate the model's sensitivity to various temperature scenarios. The model predicts endemic levels of malaria in both humans and mosquitoes when constant mid-range temperature is applied. Lower constant temperatures yield lower levels of infection in the mosquito population. Conversely, when seasonal temperature cycles were applied, multiple outbreaks coinciding with periods of increased temperature were observed.