Climate change effects on the spatial and temporal distribution of extreme precipitation in the Mid-Atlantic region

Global environmental stability is significantly impacted by climate change, with an increasing frequency and intensity of extreme weather events posing substantial risks to infrastructure, ecosystems, and communities. In this study, a comprehensive analysis of extreme precipitation events in Maryland is provided, utilizing the latest CMIP6 models to assess historical (1951–2022) trends and project future scenarios from 2022 to 2100 under three Shared Socioeconomic Pathways (SSP126, SSP245, and SSP585). Extreme precipitation events were analyzed by fitting a Generalized Logistic (GLO) distribution to daily observational precipitation data, with events above the 95th percentile identified as extreme. The frequency and magnitude of these events were determined by counting annual occurrences, calculating mean magnitudes, and assessing their probability across different climate scenarios. Additionally, Depth-Duration-Frequency (DDF) curves were developed to estimate expected precipitation amounts for various durations and return periods, with a focus on the associated uncertainties. The findings indicate that the probability of extreme precipitation events has slightly increased over the past seven decades, with notable variability between years, while the magnitude of these events has remained relatively stable. Future projections suggest a considerable rise in both the frequency and magnitude of extreme events, particularly under the SSP585 scenario, with the most severe impacts expected in certain regions of Maryland (Anne Arundel County and Dorchester County) and on the global level. This highlights the varying degrees of vulnerability across the state and underscores the need for targeted adaptation strategies. These results emphasize the critical importance of emissions mitigation to limit the severity of future extreme weather events and suggest that robust, region-specific adaptation measures will be essential in managing the increasing risks associated with climate change. • Extreme precipitation events were obtained using probability trend for diverse SSPs. • GLO distributions are powerful to identify extreme events and compute DDF curves. • Rising Extreme events pose high risks under historical and future climate scenarios. • Some Maryland counties need climate adaptation due to high risks of extreme events. • Mitigation strategies are urged to manage future risks of extreme events.