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Land managers need consistent information about the geographic distribution of wildland fuels and forest structure over large areas to evaluate fire risk and plan fuel treatments. We compared spatial predictions for 12 fuel and forest structure variables across three regions in the western United States using gradient nearest neighbor (GNN) imputation, linear models (LMs), classification and regression trees (CART), and geostatistical methods (kriging and universal kriging (UK)). Local-scale map accuracy varied considerably across sites, variables, and methods. GNN performed best for forest structure variables in Oregon, but LMs and UK were better for canopy variables and for forest structure variables in Washington and California. Kriging performed poorly throughout, and kriging did not improve prediction accuracy when added to LMs as UK. GNN outperformed CART in predicting vegetation classes and fuel models, complex variables defined by multiple attributes. Regional distributions of vegetation classes and fuel models were accurately represented by GNN and very poorly by CART and LMs. Despite their often limited accuracy at the local scale, GNN maps are useful when information on a wide range of forest attributes is needed for analysis and forest management at intermediate, i.e., landscape to ecoregional, scales.