A major environmental problem in semi-arid landscapes of western North America is the invasion of native vegetation by cheatgrass (Bromus tectorum L.), an annual Eurasian grass that covers >40 million ha of range and woodland in the western US. Cheatgrass can be especially problematic after fire—either prescribed fire or wildfire. Although cheatgrass is known to generally thrive in regions of moderate temperatures, dry summers, and reliable winter precipitation, the spatial patterns of postfire cheatgrass invasion are not well characterized at finer spatial scales (e.g., within most individual landscapes). We used boosted regression trees to develop a spatial model of cheatgrass abundance 0 yr to 19 yr postfire in an 8000 km2 semiarid landscape centered on Dinosaur National Monument (Colorado and Utah, USA). Elevation, a deterministic variable, was the strongest single predictor, with higher cheatgrass cover occurring below 1600 meters. Two other contingent variables, fire severity and climatic conditions in the year after the fire, increased the model’s predictive power. The influence of fire severity differed with the scale of analysis. Across the landscape as a whole (including extensive areas at moderate to high elevation), a greater likelihood of high postfire cheatgrass cover (≥10 %) was associated with lower fire severity. Focusing only on low-elevation areas (<1600 m), higher fire severity was associated with greater likelihood of high cheatgrass cover. Low precipitation in the year after fire was associated with greater probability of high cheatgrass cover in all areas.