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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. 

Abstract A critical task to better quantify changes in precipitation (P) mean and extreme statistics due to global warming is to gain insights into the underlying physical generating mechanisms (GMs). Here, the dominant GMs associated with daily P recorded at 2861 gauges in the Conterminous United States from 1980 to 2018 were identified from atmospheric reanalyses and publicly available datasets. The GMs include fronts (FRT), extratropical cyclones (ETC), atmospheric rivers (AR), tropical cyclones (TC), and North American Monsoon (NAM). Climatologies of the GM occurrences were developed for the nonzero P (NZP) and annual P maxima (APM) samples, characterizing the marginal and extreme P distributions, respectively. FRT is everywhere the most frequent (45-75%) GM of NZP followed by ETC (12-33%). The FRT contribution declines for APM (19-66%), which are dominated by AR (50-65%) in western regions and affected by TC (10-18%) in southern and eastern regions. The GM frequencies exhibit trends with the same signs over large regions, which are not statistically significant except for an increase in FRT (TC) frequency in the Northeast (central region). Two-sample tests showed well-defined spatial patterns with regions where (1) both the marginal and extreme P distributions of the two dominant GMs likely belong to different statistical populations, and (2) only the marginal or the extreme distributions could be considered statistically different. These results were interpreted throughL-moments and parametric distributions that adequately model NZP and APM frequency. This work provides useful insights to incorporate mixed populations and nonstationarity in P frequency analyses.