Root disease fungi are native, soil-borne pathogens that impact forest stand dynamics and productivity through direct tree mortality or reduced yield. Despite being a major contributor of mortality in forests of western North America, particularly in the northern Rocky Mountains in the United States, little is known about the underlying factors that influence root disease occurrence and severity at landscape and broader scales. We used a database with >15,000 geographically-distributed, spatially-referenced root disease assessments from United States Department of Agriculture (USDA) Forest Service Forest Inventory and Analysis (FIA) plots from across the US Northern Rocky mountains and high resolution (30 m) climate and soil-water balance grids to examine climatic and biophysical factors associated with presence and severity of root disease. We used a two-stage modeling approach that combined boosted regression-tree models and residual kriging to predict the probability of occurrence and severity of root disease across the area of analysis. A best-fit model explained root disease occurrence moderately well (AUC = 0.77) and included average annual dewpoint temperature, evapotranspiration and climatic water deficit. Root disease occurrence was associated with moist, humid sites subject to low or moderate climatic water deficits. Root disease severity was not well explained by climatic and biophysical factors alone (R2 = 0.19). Residual kriging only marginally improved model fit (R2 = 0.20). We used the fitted models to produce 30 m resolution gridded root disease occurrence and severity maps across the US Forest Service Northern Region for use in broad-scale planning and analysis. Weak spatial autocorrelation at distances beyond 5 km and relatively low accuracy of the severity model suggests that the FIA plot distribution is not suited to predict spatial patterns of root disease severity. Additional factors related to root disease severity, such as vegetation type and site-specific disturbance histories, are likely needed for fine-scale severity predictions.