Air-pollution monitoring is sparse across most of the United States, so geostatistical models are important for reconstructing concentrations of fine particulate air pollution (PM_2.5) for use in health studies. We present XGBoost-IDW Synthesis (XIS), a daily high-resolution PM_2.5 machine-learning model covering the contiguous US from 2003 through 2023. XIS uses aerosol optical depth from satellites and a parsimonious set of additional predictors to make predictions at arbitrary points, capturing near-roadway gradients and allowing the estimation of address-level exposures. We built XIS with a computationally tractable workflow for extensibility to future years, and we used weighted evaluation to fairly assess performance in sparsely monitored regions. Averaging across all years in site-level cross-validation, the weighted mean absolute error of predictions (MAE) was 2.09 μg/m³, a substantial improvement over the mean absolute deviation from the median, which was 4.15 μg/m³. Comparing XIS to a leading product from the US Environmental Protection Agency, the Fused Air Quality Surface Using Downscaling (FAQSD), we obtained a 17% reduction in MAE. We also found a stronger relationship between PM_2.5 and social vulnerability with XIS than with the FAQSD. Thus, XIS has potential for reconstructing environmental exposures, and its predictions have applications in environmental justice and human health.