Anthropogenic climate change is driving shifts in vegetation communities. These shifts are projected to increase in frequency and extent as climate change intensifies into the future. Trees are long-lived and, in the absence of disturbance, can tolerate years to decades of climate conditions for which they are maladapted. A disturbance, however, can trigger a sudden shift in vegetation type where “legacy” forests are maladapted. Identifying the level of forest maladaptation to climate and likelihood of disturbance could help land managers anticipate sudden vegetation shifts as they plan for the future. As part of the NASA CMS Project, we implemented an Environmental Evaluation Modeling System (EEMS) model to evaluate the risk for sudden forest shift in the western conterminous United States. To determine where forests will be maladapted under future conditions, we simulated vegetation without fire using the MC2 dynamic global vegetation model (DGVM) with 20 different climate scenarios. Vegetation type departure between historical and future periods is used as a proxy for the risk of vegetation shift due to maladaptation. The greater the departure, the greater the risk. To limit our analysis to actual forested areas, we used LandFire forest landcover from the U.S. Departments of Agriculture and Interior. To quantify disturbance risk, the EEMS model uses a disturbance dataset produced as part of the NASA CMS project. In addition to quantifying the risk of sudden vegetation shift, the EEMS model also provides a standalone data layer of forest maladaptation useful for decision support as well as the spatial data layers for each node in the EEMS model. To conclude, we discuss how the model can be combined with other models – e.g. species distribution models and economic models – to further inform land management decisions.