Occurrences of devastating wildfires have been increasing in the United States for the past decades. While some environmental controls, including weather, climate, and fuels, are known to play important roles in controlling wildfires, the interrelationships between these factors and wildfires are highly complex and may not be well represented by traditional parametric regressions. Here we develop a model consisting of multiple machine learning algorithms to predict 0.5∘×0.5∘ gridded monthly wildfire burned area over the south central United States during 2002–2015 and then use this model to identify the relative importance of the environmental drivers on the burned area for both the winter–spring and summer fire seasons of that region. The developed model alleviates the issue of unevenly distributed burned-area data, predicts burned grids with area under the curve (AUC) of 0.82 and 0.83 for the two seasons, and achieves temporal correlations larger than 0.5 for more than 70 % of the grids and spatial correlations larger than 0.5 (p<0.01) for more than 60 % of the months. For the total burned area over the study domain, the model can explain 50 % and 79 % of the observed interannual variability for the winter–spring and summer fire season, respectively. Variable importance measures indicate that relative humidity (RH) anomalies and preceding months' drought severity are the two most important predictor variables controlling the spatial and temporal variation in gridded burned area for both fire seasons. The model represents the effect of climate variability by climate-anomaly variables, and these variables are found to contribute the most to the magnitude of the total burned area across the whole domain for both fire seasons. In addition, antecedent fuel amounts and conditions are found to outweigh the weather effects on the amount of total burned area in the winter–spring fire season, while fire weather is more important for the summer fire season likely due to relatively sufficient vegetation in this season.