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The intensity of extreme precipitation events is projected to increase in a warmer climate 1 – 5 , posing a great challenge to water sustainability in natural and built environments. Of particular importance are rainfall (liquid precipitation) extremes owing to their instantaneous triggering of runoff and association with floods 6 , landslides 7 – 9 and soil erosion 10 , 11 . However, so far, the body of literature on intensification of precipitation extremes has not examined the extremes of precipitation phase separately, namely liquid versus solid precipitation. Here we show that the increase in rainfall extremes in high-elevation regions of the Northern Hemisphere is amplified, averaging 15 per cent per degree Celsius of warming—double the rate expected from increases in atmospheric water vapour. We utilize both a climate reanalysis dataset and future model projections to show that the amplified increase is due to a warming-induced shift from snow to rain. Furthermore, we demonstrate that intermodel uncertainty in projections of rainfall extremes can be appreciably explained by changes in snow–rain partitioning (coefficient of determination 0.47). Our findings pinpoint high-altitude regions as ‘hotspots’ that are vulnerable to future risk of extreme-rainfall-related hazards, thereby requiring robust climate adaptation plans to alleviate potential risk. Moreover, our results offer a pathway towards reducing model uncertainty in projections of rainfall extremes. The recent and projected future increase in rainfall extremes in high-elevation areas of the Northern Hemisphere is due to a warming-induced shift from snow to rain.

Increasing wildfire and declining snowpacks in mountain regions threaten water availability. We combine satellite‐based fire detections with snow seasonality classifications to examine fire activity in California's seasonal and ephemeral snow zones. We find a nearly tenfold increase in fire activity during 2020–2021 versus 2001–2019. Accumulation season broadband snow albedo declined 25%–71% at two burned sites (2021 and 2022) according to in‐situ data relative to un‐burned conditions, with greater declines associated with increased burn severity. By enhancing snowpack susceptibility to melt, both decreased snow albedo and canopy drove midwinter melt during a multi‐week dry spell in 2022. Despite similar meteorological conditions in December–February 2013 and 2022–linked to persistent high pressure weather regimes–minimal melt occurred in 2013. Post‐fire snowpack differences are confirmed with satellite measurements. With growing geographical overlap between wildfire and snow, our findings suggest California's snowpack is increasingly vulnerable to the compounding effects of dry spells and wildfire. Plain Language Summary Satellite fire detections indicate substantial increases in wildfire activity in California's snow‐covered landscapes during 2020 and 2021, suggesting wildfire is increasingly altering mountain hydrology. During 2022, a multi‐week mid‐winter drought, or dry spell, occurred. A meteorologically‐similar dry spell occurred in 2013, and the 2022 event provides a test case to examine how post‐fire changes (canopy loss and deposition of burned dark material on snowpack) alter snowmelt patterns. Using field observations, weather station data, and satellite remote sensing of snow, we find large reductions in snow albedo and canopy cover drove rapid melt during the 2022 dry spell in burned areas whereas during 2013, minimal melt occurred. The societal connection between mountains and humans will be strained as mountains face increasing climate‐related stressors. Midwinter drought, snow loss, and increasing wildfire are expectations of a warming world. Addressing these challenges requires innovative water and forest management paradigms. Our findings motivate additional research into assessing and planning for post‐fire hydrologic changes in snow‐dominated landscapes as both wildfire and dry spells will increase in frequency with climate warming. Key Points A 9.8x increase in satellite fire detections in California's snow zones in 2020–2021 versus 2001–2019 implies growing overlap in fire and snow Post‐fire accumulation season broadband snow albedo declined 25%–71%, driving fewer snow‐covered days and lower snow‐cover fraction Compared with the meteorologically similar 2013 dry spell, albedo and canopy declines led to rapid midwinter melt in 2022

It has become increasingly common for researchers to utilize methods that identify weather features in climate models. There is an increasing recognition that the uncertainty associated with choice of detection method may affect our scientific understanding. For example, results from the Atmospheric River Tracking Method Intercomparison Project (ARTMIP) indicate that there are a broad range of plausible atmospheric river (AR) detectors and that scientific results can depend on the algorithm used. There are similar examples from the literature on extratropical cyclones and tropical cyclones. It is therefore imperative to develop detection techniques that explicitly quantify the uncertainty associated with the detection of events. We seek to answer the following question: given a “plausible” AR detector, how does uncertainty in the detector quantitatively impact scientific results? We develop a large dataset of global AR counts, manually identified by a set of eight researchers with expertise in atmospheric science, which we use to constrain parameters in a novel AR detection method. We use a Bayesian framework to sample from the set of AR detector parameters that yield AR counts similar to the expert database of AR counts; this yields a set of “plausible” AR detectors from which we can assess quantitative uncertainty. This probabilistic AR detector has been implemented in the Toolkit for Extreme Climate Analysis (TECA), which allows for efficient processing of petabyte-scale datasets. We apply the TECA Bayesian AR Detector, TECA-BARD v1.0.1, to the MERRA-2 reanalysis and show that the sign of the correlation between global AR count and El Niño–Southern Oscillation depends on the set of parameters used.

In this study, the resolution dependence of the simulated Greenland ice sheet surface mass balance (GrIS SMB) in the variable-resolution Community Earth System Model (VR-CESM) is investigated. Coupled atmosphere–land simulations are performed on two regionally refined grids over Greenland at 0.5∘ (∼55 km) and 0.25∘ (∼28 km), maintaining a quasi-uniform resolution of 1∘ (∼111 km) over the rest of the globe. On the refined grids, the SMB in the accumulation zone is significantly improved compared to airborne radar and in situ observations, with a general wetting (more snowfall) at the margins and a drying (less snowfall) in the interior GrIS. Total GrIS precipitation decreases with resolution, which is in line with best-available regional climate model results. In the ablation zone, CESM starts developing a positive SMB bias with increased resolution in some basins, notably in the east and the north. The mismatch in ablation is linked to changes in cloud cover in VR-CESM, and a reduced effectiveness of the elevation classes subgrid parametrization in CESM. Overall, our pilot study introduces VR-CESM as a new tool in the cryospheric sciences, which could be used to dynamically downscale SMB in scenario simulations and to force dynamical ice sheet models through the CESM coupling framework.

The 1997 New Year's flood event was the most costly in California's history. This compound extreme event was driven by a category 5 atmospheric river that led to widespread snowmelt. Extreme precipitation, snowmelt, and saturated soils produced heavy runoff causing widespread inundation in the Sacramento Valley. This study recreates the 1997 flood using the Regionally Refined Mesh capabilities of the Energy Exascale Earth System Model (RRM‐E3SM) under prescribed ocean conditions. Understanding the processes causing extreme events informs practical efforts to anticipate and prepare for such events in the future, and also provides a rich context to evaluate model skill in representing extremes. Three California‐focused RRM grids, with horizontal resolution refinement of 14 km down to 3.5 km, and six forecast lead times, 28 December 1996 at 00Z through 30 December 1996 at 12Z, are assessed for their ability to recreate the 1997 flood. Planetary to synoptic scale atmospheric circulations and integrated vapor transport are weakly influenced by horizontal resolution refinement over California. Topography and mesoscale circulations, such as the Sierra barrier jet, are better represented at finer horizontal resolutions resulting in better estimates of storm total precipitation and storm duration snowpack changes. Traditional time‐series and causal analysis frameworks are used to examine runoff sensitivities state‐wide and above major reservoirs. These frameworks show that horizontal resolution plays a more prominent role in shaping reservoir inflows, namely the magnitude and time‐series shape, than forecast lead time, 2‐to‐4 days prior to the 1997 flood onset. Plain Language Summary The 1997 California New Year's flood event caused over a billion dollars in damages. This storm became a central part in guiding efforts to reduce flood risks. Earth system models are increasingly asked to recreate extreme weather events. However, the ability of Earth system models to recreate such events requires rigorous testing. Testing ensures that models provide value in anticipating and planning for future flood events. This is particularly important given the changing climate. We evaluated the Department of Energy's flagship Earth system model, the Energy Exascale Earth System Model, in its ability to recreate the weather and flood characteristics of the 1997 flood. The model resolution, important for resolving mountain terrain and storm interactions, and forecast lead time, important for storm progression accuracy, are assessed. The multi‐forecast average from the highest‐resolution model best recreates the observed precipitation, snowpack changes, and flood characteristics. Our findings provide confidence that the highest resolution model could be used to study how a 1997‐like flood event would be altered in a warmer world. Key Points Energy Exascale Earth System Model forecasts at 3.5 km grid spacing skillfully recreate the hydrometeorology of California's 1997 flood Horizontal resolution alters the representation of key flood drivers such as the Sierra barrier jet, precipitation extremes, and snowmelt Forecast lead time 2‐to‐4 days prior to the onset of the 1997 flood minimally influences forecast precipitation and snowmelt skill

Mountains are natural dams that impede atmospheric moisture transport and water towers that cool, condense, and store precipitation. They are essential in the western United States where precipitation is seasonal, and snowpack is needed to meet water demand. With anthropogenic climate change increasingly threatening mountain snowpack, there is a pressing need to better understand the driving climatological processes. However, the coarse resolution typical of modern global climate models renders them largely insufficient for this task, and signals a need for an advanced strategy. This paper continues the assessment of variable‐resolution in the Community Earth System Model (VR‐CESM) in modeling mountain hydroclimatology to understand the role of grid‐spacing at 55, 28, 14, and 7 km and microphysics, specifically the Morrison and Gettelman (, MG1, https://doi.org/10.1175/2008JCLI2105.1) scheme versus the Gettelman and Morrison (, MG2, https://doi.org/10.1175/JCLI-D-14-00102.1) scheme. Eight VR‐CESM simulations were performed from 1999 to 2015 with the F_AMIP_CAM5 component set, which couples the atmosphere‐land models and prescribes ocean data. Refining horizontal grid‐spacing from 28 to 7 km with the MG1 scheme did not improve the simulated mountain hydroclimatology. Substantial improvements occurred with the use of MG2 at grid‐spacings ≤28 km compared to MG1 as shown with subsequent statistics. Average SWE bias diminished by 9.4X, 4.9X, and 3.5X from 55 to 7 km. The range in minimum (maximum) DJF spatial correlations increased by 0.1–0.2 in both precipitation and SWE. Mountain windward/leeward distributions and elevation profiles improved across hydroclimate variables, however not always with model resolution alone. Disconcertingly, all VR‐CESM simulations exhibited a systemic mountain cold bias that worsened with elevation and will require further examination. Key Points As long as diagnostic precipitation in microphysics schemes is used, model grid‐spacing provides essentially no benefit at scales <28 km Spatial distributions in mountain hydroclimatology improved using a prognostic precipitation microphysics scheme and refined grid‐resolution A mountain cold bias was found across all eight variable‐resolution Community Earth System Model simulations from 55 to 7 km grid‐spacing

Snowpack loss in midlatitude mountains is ubiquitously projected by Earth system models, though the magnitudes, persistence, and time horizons of decline vary. Using daily downscaled hydroclimate and snow projections, we examine changes in snow seasonality across the U.S. Pacific Southwest region during a simulated severe 20-year dry spell in the 21st century (2051–2070) developed as part of the 4th California Climate Change Assessment to provide a “stress test” for water resources. Across California’s mountains, substantial declines (30–100% loss) in median peak annual snow water equivalent accompany changes in snow seasonality throughout the region compared to the historic period. We find that 80% of historic seasonal snowpacks transition to ephemeral conditions. Subsetting empirical-statistical wildfire projections for California by snow seasonality transition regions indicates a two-to-four-fold increase in the area burned, consistent with recent observations of high elevation wildfires following extended drought conditions. By analyzing six of the major California snow-fed river systems, we demonstrate snowpack reductions and seasonality transitions result in concomitant declines in annual runoff (47–58% of historical values). The negative impacts to statewide water supply reliability by the projected dry spell will likely be magnified by changes in snowpack seasonality and increased wildfire activity.

Climate change will impact western USA water supplies by shifting precipitation from snow to rain and driving snowmelt earlier in the season. However, changes at the regional-to-mountain scale is still a major topic of interest. This study addresses the impacts of climate change on mountain snowpack by assessing historical and projected variable-resolution (VR) climate simulations in the community earth system model (VR-CESM) forced by prescribed sea-surface temperatures along with widely used regional downscaling techniques, the coupled model intercomparison projects phase 5 bias corrected and statistically downscaled (CMIP5-BCSD) and the North American regional climate change assessment program (NARCCAP). The multi-model RCP8.5 scenario analysis of winter season SWE for western USA mountains indicates by 2040-2065 mean SWE could decrease −19% (NARCCAP) to −38% (VR-CESM), with an ensemble median change of −27%. Contrary to CMIP5-BCSD and NARCCAP, VR-CESM highlights a more pessimistic outcome for western USA mountain snowpack in latter-parts of the 21st century. This is related to temperature changes altering the snow-albedo feedback, snowpack storage, and precipitation phase, but may indicate that VR-CESM resolves more physically consistent elevational effects lacking in statistically downscaled datasets and teleconnections that are not captured in limited area models. Overall, VR-CESM projects by 2075–2100 that average western USA mountain snowfall decreases by −30%, snow cover by −44%, SWE by −69%, and average surface temperature increase of +5.0 ∘C. This places pressure on western USA states to preemptively invest in climate adaptation measures such as alternative water storage, water use efficiency, and reassess reservoir storage operations.

The water cycle is an important component of the earth system and it plays a key role in many facets of society, including energy production, agriculture, and human health and safety. In this study, the Energy Exascale Earth System Model version 1 (E3SMv1) is run with low‐resolution (roughly 110 km) and high‐resolution (roughly 25 km) configurations—as established by the High Resolution Model Intercomparison Project protocol—to evaluate the atmospheric and terrestrial water budgets over the conterminous United States (CONUS) at the large watershed scale. The warm season water cycle slows down in the HR experiment relative to the LR, with decreasing fluxes of precipitation, evapotranspiration, atmospheric moisture convergence, and runoff. The reductions in these terms exacerbate biases for some watersheds, while reducing them in others. For example, precipitation biases are exacerbated at HR over the Eastern and Central CONUS watersheds, while precipitation biases are reduced at HR over the Western CONUS watersheds. The most pronounced changes with resolution to the water cycle come from reductions in precipitation and evapotranspiration. The reduction in evapotranspiration reduces the biases across nearly all of the CONUS. Additional exploratory metrics show improvements to water cycle extremes (both in precipitation and streamflow), fractional contributions of different storm types to total precipitation, and mountain snowpack. Plain Language Summary This study seeks to better understand how the U.S. DOE's Earth system model, Energy Exascale Earth System Model, simulates the conterminous United States (CONUS) water cycle. To accomplish this goal, we examine the atmosphere and land water budget terms at the watershed and seasonal space and time scales. At higher resolution and during the warm season, all of the terms in the water budget become smaller: precipitation, evapotranspiration, moisture convergence, and runoff. The reductions in evapotranspiration lead to improvements over nearly the entire CONUS, while other terms show mixed results when increasing resolution. We also examine exploratory metrics with expected resolution sensitivity—including precipitation and streamflow extremes, storm events, and snowpack—and find modest improvements. Key Points The water cycle slows down (decreased fluxes) when grid spacing has a four times refinement HR generally improves evapotranspiration, but precipitation and other terms have mixed results HR improves precipitation extremes, storm event precipitation contributions, and mountain snowpack

Atmospheric rivers (ARs) are key drivers of extreme precipitation in the Western U.S. Using regionally downscaled thermodynamic global warming (TGW) simulations, we examine how ARs with varying wind and moisture characteristics respond to warming. We classified 812 historical AR events into Gusty‐Wet, Gusty‐Dry, Calm‐Wet, and Calm‐Dry groups to evaluate differences in precipitation behavior. ARs with stronger winds and higher moisture content exhibit higher precipitation efficiency (PE) and greater integrated water vapor (IWV). Regionally, Calm ARs show higher IWV accumulation due to slower inland transport and reduced PE. Projections indicate increases in storm‐total (sub‐Clausius‐Clapeyron (CC) scaling) and maximum 3‐hourly precipitation (super‐CC scaling) across all groups, with the most pronounced changes in Gusty‐Wet and Calm‐Wet ARs. Spatial differences in surface runoff, PE, and inland reach highlight the importance of AR subtype in shaping future flood hazards. These results offer insights into event‐level and regional‐scale precipitation changes under evolving environmental conditions.