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As the climate warms, snow‐dominated regions are increasingly susceptible to precipitation phase changes and earlier snowmelt. In alpine catchments, snowmelt is the majority of runoff, governed by heterogeneous interactions among snowfall, wind, topography and vegetation. The role of the alpine snowpack in delaying the downstream release of accumulated snowfall remains understudied, despite its fundamental role in sustaining lower‐elevation ecosystems and populations. We evaluated fine‐scale changes in the magnitude and duration of alpine snow water storage and release by comparing effective precipitation seasonality to that of surface water inputs using a Snow Storage Index (SSI). We forced the Distributed Hydrology Soil Vegetation Model with historical climate conditions and an end‐of‐century warming signal down‐scaled from a 4‐km regional climate model, to simulate future changes in snow water storage and partitioning of precipitation to runoff and evapotranspiration. The hydrological modelling was conducted on a 2‐m spatial grid to resolve fine scale processes over a 0.6‐km2 headwater catchment within the Niwot Ridge Long‐Term Ecological Research site located in the Rocky Mountains, Colorado, USA. Proportionally more rainfall, reduced snowpack size and extent, and earlier snowmelt occurred in the future scenario with decreased catchment average SSI and thus snow water storing capabilities, with inordinately large decreases occurring in areas of the catchment with reconstructed snowdrifts (−57% average and −100% maximum). Using the Budyko framework to evaluate annual hydrologic partitioning, precipitation inputs to streamflow increased by a maximum 10% in large SSI regions with snowdrifts under warming conditions, signifying a potential buffer for total catchment and annual streamflow loss in a future climate. The fine‐scale analysis of hydrologic sensitivities to warming presented herein are important because climate conditions are expected to further reduce alpine snow water storage, alter hydrologic partitioning, and runoff generation for ecosystems and people living downstream. [ABSTRACT FROM AUTHOR]

Extreme precipitation events are projected to increase in frequency across much of the land‐surface as the global climate warms, but such projections have typically relied on coarse‐resolution (100–250 km) general circulation models (GCMs). The ensemble of HighResMIP GCMs presents an opportunity to evaluate how a more finely resolved atmosphere and land‐surface might enhance the fidelity of the simulated contribution of large‐magnitude storms to total precipitation, particularly across topographically complex terrain. Here, the simulation of large‐storm dominance, that is, the number of wettest days to reach half of the total annual precipitation, is quantified across the western United States (WUS) using four GCMs within the HighResMIP ensemble and their coarse resolution counterparts. Historical GCM simulations (1950–2014) are evaluated against a baseline generated from station‐observed daily precipitation (4,803 GHCN‐D stations) and from three gridded, observationally based precipitation data sets that are coarsened to match the resolution of the GCMs. All coarse‐resolution simulations produce less large‐storm dominance than in observations across the WUS. For two of the four GCMs, bias in the median large‐storm dominance is reduced in the HighResMIP simulation, decreasing by as much as 62% in the intermountain west region. However, the other GCMs show little change or even an increase (+28%) in bias of median large‐storm dominance across multiple sub‐regions. The spread in differences with resolution amongst GCMs suggests that, in addition to resolution, model structure and parameterization of precipitation generating processes also contribute to bias in simulated large‐storm dominance. Plain Language Summary: As global temperatures rise, there's growing concern about an increase in the frequency of extreme precipitation events. However, projections of future changes to large‐magnitude precipitation events often rely on coarse‐resolution models which simulate meteorology on a spatial scale larger than most storms. To address this, we explored the potential of higher resolution models from the Coupled Model Intercomparison Project HighResMIP ensemble to improve the fidelity of simulated precipitation variability across the western United States. We compared four HighResMIP models with their coarse‐resolution counterparts from 1950 to 2014 and found that coarse‐resolution models consistently underestimated the dominance of large storms compared to observed data. While some HighResMIP models showed improvement, others remained unchanged or even showed increased bias. This suggests that besides spatial resolution alone, factors like model structure and numerical representation of fine scale precipitation processes play a role in accurately simulating intense storms. These findings highlight the importance of considering both resolution and climate model structure in predicting future extreme precipitation events, especially in regions where precipitation is generated by processes that operate on a wide range of spatial scales. Key Points: Increasing spatial resolution of general circulation models (GCMs) does not consistently improve simulation of large‐storm dominance across the western United StatesCoarse resolution GCMs simulate large‐storm dominance well in regions where the largest daily precipitation events are due to synoptic scale precipitationModel structure and parameterization of precipitation generation appear to be equally as important as resolution in the simulation of large‐storm dominance [ABSTRACT FROM AUTHOR]

Lakes provide important water resources and many essential ecosystem services. Some of Earth's largest lakes recently reached record‐low levels, suggesting increasing threats from climate change and anthropogenic activities. Yet, continuous monitoring of lake levels is challenging at a global scale due to the sparse in situ gauging network and the limited spatial or temporal coverage of satellite altimeters. A few pioneering studies used water areas and hypsometric curves to reconstruct water levels but suffered from large uncertainties due to the lack of high‐quality hypsometry data. Here, we propose a novel proxy‐based method to reconstruct multi‐decadal water levels from 1992 to 2018 for both large and small lakes using Landsat images and ICESat (2003–2009) and recently launched ICESat‐2 (2018+) laser altimeters. Using the new method, we evaluate reconstructed levels of 342 lakes worldwide, with sizes ranging from 1 to 81,844 km2. Reconstructed water levels have a median root‐mean‐square error (RMSE) of 0.66 m, equivalent to 57% of the standard deviation of monthly level variability. Compared with two recently reconstructed water level data sets, the proposed method reduces the median RMSE by 27%–32%. The improvement is attributable to the new method's robust construction of high‐quality hypsometry, with a median R2 value of 0.92. Most reconstructed water level time series have a bi‐monthly or higher frequency. Given that ICESat‐2 and Landsat can observe hundreds of thousands of water bodies, this method can be applied to conduct an improved global inventory of time‐varying lake levels and thus inform water resource management more broadly than existing methods. Key Points: Landsat images and laser altimeters were leveraged to reconstruct multi‐decadal lake levels of both large and small lakesReconstructed water levels were validated against observed levels on 342 global lakes with a median error of 0.66 mMost of the reconstructed lake level time series have a bi‐monthly or higher frequency [ABSTRACT FROM AUTHOR]