Conventional Probable Maximum Precipitation (PMP) methods face several limitations, including the lack of statistical uncertainty characterization, subjectivity in storm maximization, and the assumption of a stationary climate. To address these limitations, we propose a nonstationary PMP approach that combines the novel stochastic spatiotemporal rainfall generation model StormLab with a nonstationary Generalized Extreme Value (GEV) model. We applied the new approach to the Upper Red River Basin in the south-central United States. StormLab provided 10,000 years of high-resolution (6-hour, 0.03°) precipitation fields from 1901 to 2100, based on 50 ensembles of a global climate model (GCM). A nonstationary GEV model was fitted to the simulated precipitation annual maxima, providing PMP estimates under different climate periods with an associated annual exceedance probability (AEP). The simulated precipitation was then integrated with a hydrologic model to generate annual peak discharge in major tributaries and to estimate the probable maximum flood (PMF). Our approach produces PMP estimates for areas ranging from 10-20,000 mi2 and durations from 6 to 360 hours. Results show a 16-25% increase in PMP with an AEP of 10-4 from 2020 to 2100 at different spatial and temporal scales. Higher increases of 35% and 37% are projected in PMF with the same AEP in two major tributaries. The PMP and PMF results were further compared with previous PMP/PMF estimates. This study demonstrates the value of utilizing stochastic rainfall models and GCM large ensembles to inform PMP and PMF analysis in a changing climate.

Rainfall frequency analyses are presented for the Baltimore Metropolitan region based on a 22-year, high-resolution radar rainfall data set. Analyses focus on spatial heterogeneities and time trends in sub-daily rainfall extremes. The rainfall data set covers a domain of 4900 $km^2$, has a spatial resolution of approximately 1 km and a time resolution of 15 minutes. The data set combines reflectivity-based rainfall fields during the period from 2000 - 2015 and operational polarimetric rainfall fields for the period from 2012 - 2021. Analyses of rainfall fields during the 2012 - 2015 overlap period provide grounding for assessing time trends in rainfall frequency. There are pronounced spatial gradients in short-duration rainfall extremes over the study region, with peak values of rainfall between Baltimore City and Chesapeake Bay. Rainfall frequency analyses using both peaks-over-threshold and annual peak methods point to increasing trends in short-duration rainfall extremes over the period from 2000 to 2021. Intercomparisons of sub-daily rainfall extremes with daily extremes show significant differences. Less than 50$\% $ of annual maximum hourly values occur on the same day as the daily maximum and there is relatively weak correlation between magnitudes when the hourly and daily maximum overlap. Changing measurement properties are a key challenge for application of radar rainfall data sets to detection of time trends. Mean field bias correction of radar rainfall fields using rain gauge observations is both an important component of the 22-year rainfall data set and a useful tool for addressing problems associated with changing radar measurement properties.