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Historical and projected trends in extreme precipitation events are examined in Coupled Model Intercomparison Project 5 (CMIP5) models and observations, over the contiguous United States (CONUS), using several approaches. This study updates earlier studies that have used the extreme precipitation index (EPI) to assess observations and goes further by using the EPI to evaluate available climate model simulations. An increasing trend over the CONUS was found in the EPI, with large differences among seven subregions of the United States. Median of CMIP5 simulations also finds an increasing trend in the EPI, but with a smaller magnitude than the observations. Model spread is large and in most cases bigger than the model signal itself. Statistically significant (95th confidence level) increasing trends in the observational-based EPI occur over the Midwest and Eastern regions, while most decreasing trends occur over Western regions. Some models give negative correlation coefficients relative to observations. However, some ensemble members, for most models, show correlation coefficients greater than 0.5. Projections of extreme precipitation event frequency, for representative concentration pathway (RCP) scenarios 4.5 and 8.5, show increasing trends over the CONUS. Both scenarios give a steady increase throughout the period but the RCP 4.5 signal is smaller in magnitude. Overall, the RCP scenarios show an increase across all regions with the exception of some variability between decades in some regions for RCP 4.5. For the CONUS model spread is smaller than the projected signal. Regional analyses show overall agreement among models of a future increase in extreme precipitation event frequency over most regions.

Understanding how the frequency and intensity of extreme precipitation events are changing is important for regional risk assessments and adaptation planning. Here we use observational data and an ensemble of climate change model experiments (from the Coupled Model Intercomparison Project Phase 5 (CMIP5)) to examine past and potential future seasonal changes in extreme precipitation event frequency over the United States. Using the extreme precipitation index as a metric for extreme precipitation change, we find key differences between models and observations. In particular, the CMIP5 models tend to overestimate the number of spring events and underestimate the number of summer events. This seasonal shift in the models is amplified in projections. These results provide a basis for evaluating climate model skill in simulating observed seasonality and changes in regional extreme precipitation. Additionally, we highlight key sources of variability and uncertainty that can potentially inform regional impact analyses and adaptation planning.

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