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Abstract There is high confidence that extreme precipitation will increase in most areas if the globe continues to warm. In the US, NOAA Atlas 14 (NA14) is the most authoritative source for heavy rainfall frequency values used in infrastructure planning and design. However, NA14 assumes a stationary climate and uses only historical observations to estimate values. Thus, use of such values for design may lead to underperformance of long-lived infrastructure, thereby placing people and property at increased risk from flooding. Analyses of global climate model (GCM) simulations suggest that projected extreme precipitation changes will be positive nearly everywhere in the US and will be larger for shorter durations, lower annual exceedance probabilities (AEPs), and higher emissions. Herein, we provide adjustment factors that can be applied to observations-based precipitation frequency values to estimate potential future changes under selected global warming levels. These are derived from two statistically downscaled daily precipitation datasets (STAR and LOCA2) developed using modern methods that focus in part on modeling the high tail of the precipitation distribution with a high degree of fidelity. These datasets, each consisting of 16 ensemble members downscaled from a common set of 16 CMIP6 GCMs, provide estimates for durations of daily and longer. The set of adjustment factors are extended using seven models from the NA-CORDEX suite of dynamically downscaled simulations by analyzing the change in adjustment factors from daily to hourly durations. There is an average increase in the adjustment factors of about 1.3. This factor is applied to the daily adjustment factors from STAR and LOCA2 to produce estimates for the hourly duration.