Dynamical downscaling remains a powerful tool for studying regional climate processes, and the genesis of high‐resolution historical and future climate data. This technique is particularly important over areas of complex terrain, such as the western United States (WUS), where global models are especially limited in representing regional climate. After identifying a suite of WRF options that best simulate snow and precipitation for an average water year (2010) over the WUS, we evaluate the performance of the dynamically downscaled European Centre for Medium‐range Weather Forecasting's fifth Reanalysis (ERA5) from 1980 to 2020 on 45‐km, 9‐km, and two 3‐km grids. We find that by decreasing the horizontal grid spacing within WRF, improvements to Sierra Nevada and Northern Rocky Mountain snow, Santa Ana and Diablo winds, and coastal meteorology occur. For landfalling atmospheric rivers (ARs), the downscaled reanalysis simulates greater upstream integrated vapor transport (IVT) than ERA5. However, WRF skillfully simulates the positioning of the IVT and the timing and magnitude of AR precipitation. This potential IVT bias, in conjunction with increasing resolution, leads to a wet precipitation bias across the Sierra Nevada in the 3‐km experiment. This conclusion is supported by streamflow analysis, although we note that the bias in the 3‐km experiment can also be explained by in situ undercatch issues. Meanwhile, the 9‐km experiment is more biased than the 3‐km experiment across the Northern Rocky Mountains compared to in situ measured SWE and precipitation, indicating a geographic sensitivity to biases.

Southern California is a biodiversity hotspot and home to over 23 million people. Over recent decades the annual wildfire area in the coastal southern California region has not significantly changed. Yet how fire regime will respond to future anthropogenic climate change remains an important question. Here, we estimate wildfire probability in southern California at station scale and daily resolution using random forest algorithms and downscaled earth system model simulations. We project that large fire days will increase from 36 days/year during 1970–1999 to 58 days/year under moderate greenhouse gas emission scenario (RCP4.5) and 71 days/year by 2070–2099 under a high emission scenario (RCP8.5). The large fire season will be more intense and have an earlier onset and delayed end. Our findings suggest that despite the lack of a contemporary trend in fire regime, projected greenhouse gas emissions will substantially increase the fire danger in southern California by 2099.

Irrigation is a widely used water management practice that is often poorly parameterized in land surface and climate models. Previous studies have addressed this issue via use of irrigation area, applied water inventory data, or soil moisture content. These approaches have a variety of drawbacks including data latency, accurately prescribing irrigation intensity, and a lack of conservation of water volume for models using a prescribed soil moisture approach. In this study, we parameterize irrigation fluxes using satellite observations of evapotranspiration (ET) compared to ET from a suite of land surface models without irrigation. We then incorporate the irrigation flux into the Community Land Model (CLM) and use a systematic trial-and-error procedure to determine the ground- and surface-water withdrawals that are necessary to balance the new irrigation flux. The resulting CLM simulation with irrigation produces ET that matches the magnitude and seasonality of observed satellite ET well, with a mean difference of 6.3 mm month⁻¹ and a correlation of 0.95. Differences between the new CLM ET values and satellite-observed ET values are always less than 30 mm month⁻¹ and the differences show no pattern with respect to seasonality. The results reinforce the importance of accurately parameterizing anthropogenic hydrologic fluxes into land surface and climate models to assess environmental change under current and future climates and land management regimes.

Predicting future climate change over a region of complex terrain, such as the western United States (US), remains challenging due to the low resolution of global climate models (GCMs). Yet the climate extremes of recent years in this region, such as floods, wildfires, and drought, are likely to intensify further as climate warms, underscoring the need for high-quality and high-resolution predictions. Here, we present an ensemble of dynamically downscaled simulations over the western US from 1980–2100 at 9 km grid spacing, driven by 16 latest-generation GCMs. This dataset is titled the Western US Dynamically Downscaled Dataset (WUS-D3). We describe the challenges of producing WUS-D3, including GCM selection and technical issues, and we evaluate the simulations' realism by comparing historical results to temperature and precipitation observations. The future downscaled climate change signals are shaped in physically credible ways by the regional model's more realistic coastlines and topography. (1) The mean warming signals are heavily influenced by more realistic snowpack. (2) Mean precipitation changes are often consistent with wetting on the windward side of mountain complexes, as warmer, moister air masses are uplifted orographically during precipitation events. (3) There are large fractional precipitation increases on the lee side of mountain complexes, leading to potentially significant changes in water resources and ecology in these arid landscapes. (4) Increases in precipitation extremes are generally larger than in the GCMs, driven by locally intensified atmospheric updrafts tied to sharper, more realistic gradients in topography. (5) Changes in temperature extremes are different from what is expected by a shift in mean temperature and are shaped by local atmospheric dynamics and land surface feedbacks. Because of its high resolution, comprehensiveness, and representation of relevant physical processes, this dataset presents a unique opportunity to evaluate societally relevant future changes in western US climate.

Amplified and persistent ridges in western North America are recurring features associated with drought conditions in California. The recent drought event (2012–2016) lasted through both La Niña and El Niño episodes, suggesting additional climate drivers are important in addition to the commonly perceived El Niño‐Southern Oscillation. Diagnostic analyses presented here suggest that, while the Pacific North American (PNA) and North Pacific Oscillation (NPO) do not directly cause drought in California, the relationships between them and with the upper air circulation pattern do modulate the spatial drought pattern. The positive PNA relative circulation leads drier northern California, and (‐NPO) relative circulation leads southern California to be drier. The types of drought in this region emerge mostly from the combination of two PNA and NPO relative oceanic and atmospheric oscillations. At present, climate model projections do not indicate any significant change in these particular drought‐modulating processes.

In January 2016, a robust reversal of the Arctic Oscillation took place associated with a rapid tropospheric warming in the Arctic region; this was followed by the occurrence of a classic sudden stratospheric warming in March. The succession of these two distinct Arctic warming events provides a stimulating opportunity to examine their characteristics in terms of similarities and differences. Historical cases of these two types of Arctic warming were identified and validated based upon tropical linkages with the Madden‐Julian Oscillation and El Niño as documented in previous studies. The analysis indicates a recent and seemingly accelerated increase in the tropospheric warming type versus a flat trend in stratospheric warming type. The shorter duration and more rapid transition of tropospheric warming events may connect to the documented increase in midlatitude weather extremes, more so than the route of stratospheric warming type. Forced simulations with an atmospheric general circulation model suggest that the reduced Arctic sea ice contributes to the observed increase in the tropospheric warming events and associated remarkable strengthening of the cold Siberian high manifest in 2016.