Coral reefs occupy only 0.1% of the Earth’s surface area, but support 30% of marine biodiversity, reduce wave energy contributing to storm surge by 97%, and provide over $8.5 billion to the annual economy in south Florida. These vital reefs are in decline, however, due to rising ocean temperatures causing corals to expel their algal symbionts, e.g. coral bleaching, leading to mortality. Advanced prediction of the onset of heat stress leading to bleaching events can help restoration practitioners and management agencies allocate resources and prioritize goals prior to bleaching events. Currently, prediction of heat stress is performed via dynamical model forecasts of local sea surface temperatures, but doesn’t incorporate predictors that are specifically related to the heat stress. This work presents the first explainable machine learning prediction system of heat stress onset for Florida’s Coral Reef. Using a combination of local and global predictors, we train gradient-boosted random forests to predict when various reef cells will experience heat stress levels related to bleaching. We forgo the “black box” notion of machine learning models by utilizing explainable AI to diagnose the predictors, in ranked order, which contribute to an accurate prediction. Here, we compute the SHAPley values which attribute the contribution of each input to the final prediction. The explainability of the machine learning model’s feature importance pinpoints the most important indicators for heat stress prediction with higher confidence. By communicating with local communities, early prediction of the onset of coral heat stress allows for environmental mitigation during high stress events before coral death to promote coral resilience and local sustainability.