Many mountainous and high-latitude regions have experienced more precipitation as rain rather than snow due to warmer winter temperatures. Further decreases in the annual snow fraction are projected under continued global warming, with potential impacts on flood risk. Here, we quantify the size of streamflow peaks in response to both seasonal and event-specific rain-fraction using stream gage observations from watersheds across the western United States. Across the study watersheds, the largest rainfall-driven streamflow peaks are >2.5 times the size of the largest snowmelt-driven peaks. Using a panel regression analysis of individual precipitation and snowmelt events, we show that the empirical streamflow response grows approximately exponentially as the liquid precipitation input increases, with rain-dominated runoff leading to proportionately larger streamflow increases than snowmelt or mixed rain-and-snow runoff. We find that the response to changes in rain percentage is largest in the wettest watersheds, where wet antecedent conditions are important for increasing runoff efficiency. Similarly, the effect of rain percentage is larger across watersheds in the Northwest and West regions compared to watersheds in the Northern Rockies and Southwest regions. Overall, as a higher percentage of precipitation falls as rain, increases in the size of rainfall-driven and "rain-on-snow"-driven floods have the potential to more than offset decreases in the size of snowmelt-driven floods.

Precipitation extremes have increased across many regions of the United States, with further increases anticipated in response to additional global warming. Quantifying the impact of these precipitation changes on flood damages is necessary to estimate the costs of climate change. However, there is little empirical evidence linking changes in precipitation to the historically observed increase in flood losses. We use >6,600 reports of state-level flood damage to quantify the historical relationship between precipitation and flood damages in the United States. Our results show a significant, positive effect of both monthly and 5-d state-level precipitation on state-level flood damages. In addition, we find that historical precipitation changes have contributed approximately one-third of cumulative flood damages over 1988 to 2017 (primary estimate 36%; 95% CI 20 to 46%), with the cumulative impact of precipitation change totaling $73 billion (95% CI 39 to $91 billion). Further, climate models show that anthropogenic climate forcing has increased the probability of exceeding precipitation thresholds at the extremely wet quantiles that are responsible for most flood damages. Climate models project continued intensification of wet conditions over the next three decades, although a trajectory consistent with UN Paris Agreement goals significantly curbs that intensification. Taken together, our results quantify the contribution of precipitation trends to recent increases in flood damages, advance estimates of the costs associated with historical greenhouse gas emissions, and provide further evidence that lower levels of future warming are very likely to reduce financial losses relative to the current global warming trajectory.

Severe precipitation and flooding are widespread hazards impacting >70 million people globally each year (CRED, 2018). Climate change has increased the frequency and intensity of extreme precipitation (Diffenbaugh et al., 2017;Min et al., 2011;Papalexiou & Montanari, 2019), which increases the costs associated with these hazards (Davenport et al., 2021). To adapt to future precipitation and flooding extremes, it is critical to understand how these hazards are changing.Increasing precipitation intensity due to higher atmospheric moisture is an expected response to global warming (Allen & Ingram, 2002;Trenberth, 1999). Climate change could also cause dynamic changes such as altering the location and speed of storm tracks (O'

An ‘emergent constraint’ (EC) is a statistical relationship, across a model ensemble, between a measurable aspect of the present day climate (the predictor) and an aspect of future projected climate change (the predictand). If such a relationship is robust and understood, it may provide constrained projections for the real world. Here, Coupled Model Intercomparison Project 6 (CMIP6) models are used to revisit several ECs that were proposed in prior model intercomparisons with two aims: (1) to assess whether these ECs survive the partial out-of-sample test of CMIP6 and (2) to more rigorously quantify the constrained projected change than previous studies. To achieve the latter, methods are proposed whereby uncertainties can be appropriately accounted for, including the influence of internal variability, uncertainty on the linear relationship, and the uncertainty associated with model structural differences, aside from those described by the EC. Both least squares regression and a Bayesian Hierarchical Model are used. Three ECs are assessed: (a) the relationship between Southern Hemisphere jet latitude and projected jet shift, which is found to be a robust and quantitatively useful constraint on future projections; (b) the relationship between stationary wave amplitude in the Pacific-North American sector and meridional wind changes over North America (with extensions to hydroclimate), which is found to be robust but improvements in the predictor in CMIP6 result in it no longer substantially constrains projected change in either circulation or hydroclimate; and (c) the relationship between ENSO teleconnections to California and California precipitation change, which does not appear to be robust when using historical ENSO teleconnections as the predictor.

Quantification of the sector-specific financial impacts of historical global warming represents a critical gap in climate change impacts assessment. The multiple decades of county-level data available from the U.S. crop insurance program—which collectively represent aggregate damages to the agricultural sector largely borne by U.S. taxpayers—present a unique opportunity to close this gap. Using econometric analysis in combination with observed and simulated changes in county-level temperature, we show that global warming has already contributed substantially to rising crop insurance losses in the U.S. For example, we estimate that county-level temperature trends have contributed $US2017 27.0 billion—or 19%—of the national-level crop insurance losses over the 1991–2017 period. Further, we estimate that observed warming contributed almost half of total losses in the most costly single year (2012). In addition, analyses of a large suite of global climate model simulations yield very high confidence that anthropogenic climate forcing has increased U.S. crop insurance losses. These sector-specific estimates provide important quantitative information about the financial costs of the global warming that has already occurred (including the costs of individual extreme events), as well as the economic value of mitigation and/or adaptation options.