We developed multiple regression models and tree-based (CART -- classification and regression tree) models to predict fire return intervals across the Interior Columbia River basin at 1-km resolution, using geo-referenced fire history potential vegetation, cover type, and precipitation databases. We weighted the models based on data quality and performed a sensitivity analysis of the effects on the models of estimation errors due to lack of crossdating. The regression models predict fire return intervals from 1 to 375 years for forested areas, whereas the tree-based models predict a range of 8 to 150 years. Both types of models predict latitudinal and elevational gradients of increasing fire return intervals. Although the tree-based models explain more of the variation in the original data, the regression models are less likely to produce extrapolation errors. Thus, the models serve complementary purposes in elucidating the relationships among fire frequency, the predictor variables, and spatial scale. They also demonstrate the integration of qualitative and quantitative methods, and can be updated as better fire history data become available.