Assessing the relationships between weather patterns and the likelihood of fire occurrence in the Caribbean has not been as central to climate change research as in temperate regions, due in part to the smaller extent of individual fires. However, the cumulative effect of small frequent fires can shape large landscapes, and fire-prone ecosystems are abundant in the tropics. Climate change has the potential to greatly expand fire-prone areas to moist and wet tropical forests and grasslands that have been traditionally less fire-prone, and to extend and create more temporal variability in fire seasons. We built a machine learning random forest classifier to analyze the relationship between climatic, socio-economic, and fire history data with fire occurrence and extent for the years 2003–2011 in Puerto Rico, nearly 35,000 fires. Using classifiers based on climate measurements alone, we found that the climate space is a reliable associate, if not a predictor, of fire occurrence and extent in this environment. We found a strong relationship between occurrence and a change from average weather conditions, and between extent and severity of weather conditions. The probability that the random forest classifiers will rank a positive example higher than a negative example is 0.8–0.89 in the classifiers for deciding if a fire occurs, and 0.64–0.69 in the classifiers for deciding if the fire is greater than 5 ha. Future climate projections of extreme seasons indicate increased potential for fire occurrence with larger extents.