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Since 2008, suicide has ranked as the 10th leading cause of death in the United States (U.S.), with nearly 45 000 suicides occurring in 2016. Suicide rates vary by state, with higher rates in the West. To target prevention efforts, a detailed understanding of geographic variation is needed, however interpreting county-level suicide rates can be challenging because rates based on small numbers can be unstable and highly variable year-to-year.To use small area estimation methods to generate stable estimates of annual county-level suicide rates for 2005 through 2015 and to examine variation across counties by geography and over time.Suicides were identified from National Vital Statistics System Mortality Data using ICD-10 underlying cause codes U03, X60-X84, Y87.0. Hierarchical Bayesian models were used to estimate suicide rates for 3140 counties. Models included time-varying county-level covariates representing risk factors for suicide (demographic, socioeconomic and health- and crime-related characteristics) and included terms to account for spatial and temporal dependence. Model-based estimates were mapped to explore geographic and temporal patterns and examine urban-rural differences.From 2005 to 2015, model-based county-level suicide rates increased by at least 10% for 99% of counties, with 87% of counties showing increases of 20% or more. Counties with the highest model-based rates were consistently located across the western and northwestern U.S.; geographic patterns did not change over time. Rural counties had higher estimated rates than urban counties, and saw the largest increases in rates during the study period.Small area estimation methods can be used to overcome many of the challenges associated with examining geographic variation in suicide rates at a more granular level.Maps of model-based estimates can help target prevention efforts both within and across state boundaries, and inform research on community-level risk and protective factors for suicide mortality.