Precipitation-frequency data are essential for design storms, flood risk assessment, infrastructure design, and erosion and sediment control plans, but remain uncertain due to regional heterogeneity and climate variability. Existing precipitation-frequency products are generally based on stationary estimates and provide limited information on how extreme-precipitation frequency varies with large-scale climate conditions. Accounting for these spatial and climate variability effects in models over large domains is computationally intensive, which has limited the generation of such precipitation-frequency products. In this study, we developed a 0.1° gridded precipitation-frequency dataset, named PreXFOCUS (Precipitation eXtreme Frequency under Oscillations in Climate across the Conterminous United States; Takallou et al., 2026), using Bayesian hierarchical modeling of annual maximum daily precipitation. The dataset is available on Zenodo at <a href="https://doi.org/10.5281/zenodo.21979711" target="_blank" rel="noopener">https://doi.org/10.5281/zenodo.21979711</a>. Four model formulations were compared, ranging from independent site-specific estimation to spatially varying extreme-value parameter fields with Gaussian- copula dependence among annual maxima. Spatial pooling across all GEV parameters combined with data-level spatial dependence provided the best goodness of fit. The selected model was then used for conditional spatial simulation at held-out sites, where the resulting return levels were validated against estimates obtained from direct model fitting. These return-level estimates were subsequently incorporated into spatially varying Bernoulli occurrence models to quantify climate-conditioned changes in their exceedance probabilities. Adding MEI, NAO, and AMO to these occurrence models improved model fit relative to the time-invariant baseline and shifted exceedance probabilities by up to 40 % in some regions. The resulting products provide fine-resolution precipitation-frequency design estimates and climate-informed return levels across the CONUS, supporting hydrologic modeling, infrastructure planning, and regional flood-risk assessment.