AbstractDust storms are increasing in frequency and correlate with adverse health outcomes but remain understudied in the United States (U.S.), partially due to the limited spatio‐temporal coverage, resolution, and accuracy of current data sets. In this work, dust‐related metrics from four public areal data products were compared to a monitor‐based “gold standard” dust data set. The data products included the National Weather Service (NWS) storm event database, the Modern‐Era Retrospective analysis for Research and Applications—Version 2, the EPA's Air QUAlity TimE Series (EQUATES) Project using the Community Multiscale Air Quality Modeling System (CMAQ), and the Copernicus Atmosphere Monitoring Service global reanalysis product. California, Nevada, Utah, and Arizona, which account for most dust storms reported in the U.S., were examined. Dichotomous and continuous metrics based on reported dust storms, particulate matter concentrations (PM₁₀ and PM_2.5), and aerosol‐type variables were extracted or derived from the data products. Associations between these metrics and a validated dust storm detection method utilizing Interagency Monitoring of Protected Visual Environments monitors were estimated via quasi‐binomial regression. In general, metrics from CAMS yielded the strongest associations with the “gold standard,” followed by the NWS storm database metric. Dust aerosol (0.9–20 μm) mixing ratio, vertically integrated mass of dust aerosol (9–20 μm), and dust aerosol optical depth at 550 nm from CAMS generated the highest standardized odds ratios among all metrics. Future work will apply machine‐learning methods to the best‐performing metrics to create a public dust storm database suitable for long‐term epidemiologic studies.