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Estimating future hydrologic floods under nonstationary climate is a key challenge for flood management. Climate‐informed approaches to long‐term flood projection are an appealing alternative to traditional modeling chains. This work formalizes climate‐informed approaches into a general methodology consisting of four steps: (1) selection of predictand representing extreme events, (2) identification of credible large‐scale predictors that mechanistically control the occurrence and magnitude of the predictand, (3) development of a statistical model relating the predictors to the predictand, and (4) projection of the predictand by forcing the model with predictor projections. These four steps, developed from a review of the current literature, are demonstrated for multiple gages in the northwest Ohio River Basin in the United States Midwest as a case study. Floods are defined as annual maximum series events in January through April and are linked to geopotential height and soil moisture predictors in a Bayesian linear regression model. The projections generally show a slight decrease in future flood magnitude and demonstrate the transparency of the climate‐informed approach. An initial step for more general application across the United States and remaining challenges associated with climate‐informed flood projection are discussed.

The massive socioeconomic impacts engendered by extreme floods provides a clear motivation for improved understanding of flood drivers. We use self-organizing maps, a type of artificial neural network, to perform unsupervised clustering of climate reanalysis data to identify synoptic-scale atmospheric circulation patterns associated with extreme floods across the United States. We subsequently assess the flood characteristics (e.g., frequency, spatial domain, event size, and seasonality) specific to each circulation pattern. To supplement this analysis, we have developed an interactive website with detailed information for every flood of record. We identify four primary categories of circulation patterns: tropical moisture exports, tropical cyclones, atmospheric lows or troughs, and melting snow. We find that large flood events are generally caused by tropical moisture exports (tropical cyclones) in the western and central (eastern) United States. We identify regions where extreme floods regularly occur outside the normal flood season (e.g., the Sierra Nevada Mountains due to tropical moisture exports) and regions where multiple extreme flood events can occur within a single year (e.g., the Atlantic seaboard due to tropical cyclones and atmospheric lows or troughs). These results provide the first machine-learning based near-continental scale identification of atmospheric circulation patterns associated with extreme floods with valuable insights for flood risk management.

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