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Floods occur everywhere and in every season. Yet, most studies have focused only on annual maximum floods (AMFs), their climatology, and the associated impacts. Given that monthly/seasonal floods also cause significant damage and disruptions to daily life, this study may be the first to explore winter flood hydroclimatology, a predominantly a non-AMF season, and its associated large-scale climate drivers over the Coterminous US (CONUS). Using a mixed-effects model, we find that the influence of various hydroclimate predictors on winter floods is largely consistent within subregions. Antecedent land-surface conditions are crucial for winter floods in inland areas, while the Pacific sea surface temperatures (SSTs) significantly affects coastal watersheds. The Atlantic SSTs impact winter floods in the south and northeast, while atmospheric conditions influence the Midwest and California. Additional analysis reveals that damage from winter floods is more widespread compared to AMFs across the nation, affecting the entire eastern seaboard, Southwest US, and over the Great Lakes region. Thus, a comprehensive understanding of floods across all seasons (non-AMFs) is critical for developing effective mitigation measures, as it provides information on impacts and required compensation for smaller return period floods. 

Abstract With an increasing number of continental‐scale hydrologic models, the ability to evaluate performance is key to understanding uncertainty and making improvements to the model(s). We hypothesize that any model, running a single set of physics, cannot be “properly” calibrated for the range of hydroclimatic diversity as seen in the contenintal United States. Here, we evaluate the NOAA National Water Model (NWM) version 2.0 historical streamflow record in over 4,200 natural and controlled basins using the Nash‐Sutcliffe Efficiency metric decomposed into relative performance, and conditional, and unconditional bias. Each of these is evaluated in the contexts of meteorologic, landscape, and anthropogenic characteristics to better understand where the model does poorly, what potentially causes the poor performance, and what similarities systemically poor performing areas share. The primary objective is to pinpoint traits in places with good/bad performance and low/high bias. NWM relative performance is higher when there is high precipitation, snow coverage (depth and fraction), and barren area. Low relative skill is associated with high potential evapotranspiration, aridity, moisture‐and‐energy phase correlation, and forest, shrubland, grassland, and imperviousness area. We see less bias in locations with high precipitation, moisture‐and‐energy phase correlation, barren, and grassland areas and more bias in areas with high aridity, snow coverage/fraction, and urbanization. The insights gained can help identify key hydrological factors underpinning NWM predictive skill; enforce the need for regionalized parameterization and modeling; and help inform heterogenous modeling systems, like the NOAA Next Generation Water Resource Modeling Framework, to enhance ongoing development and evaluation.