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Despite lower‐than‐average total precipitation in the western states of the U.S., the 2021 "precipitation roller coaster" defined as large precipitation swings has pointed to a strong hydroclimatic intensity (HYINT). Here we examine the 2021 HYINT using an index—a product of the average precipitation intensity (INT) and dry spell length (DSL). HYINT exhibited an extremely high value in the western U.S. in 2021. INT and DSL contribute differently to the 2021 HYINT, with large spatial variability. Overall, the 2021 extreme HYINT in central California and Utah is tied more to large INT, than to DSL. Meanwhile, the historical trends in INT and DSL may have contributed to the extreme 2021 HYINT event. The fraction of attributable risk framework reveals that the 2021 extreme HYINT is more likely to occur with anthropogenic forcing (e.g., 7.3 times more likely for HYINT exceeding 1.3) than natural forcing alone. Plain Language Summary: The western U.S. is a hotspot for studying climate change impacts on the hydrological cycle. Despite lower‐than‐average total precipitation in 2021, the contrasting dryness and wetness in the western U.S. has been widely reported as a "precipitation roller coaster." In this paper we quantified the "precipitation roller coaster" using an index (hydroclimatic intensity [HYINT])—a product of average precipitation intensity during wet days and dry spell length (DSL). The study found that the 2021 extreme HYINT event was largely attributable to the combined impacts of precipitation intensity and DSL in California and Utah, with precipitation intensity playing a more important role. In contrast, the 2021 precipitation event in other western states exhibited divergent contributions from precipitation intensity and DSL. The southwestern U.S. has been identified as a hotspot for increasing HYINT, which is tied more to the increasing DSL than the precipitation intensity. The trends in DSL and precipitation intensity may have played a key role in driving the 2021 extreme HYINT event. Using climate model experiments with and without anthropogenic forcing, an extreme HYINT event in the western U.S. is more likely to occur with anthropogenic forcing. Key Points: Hydroclimatic intensity (HYINT) exhibited extremely high values in parts of the western U.S. in 2021, mainly caused by average precipitation intensityHYINT shows a significant rising trend in most of the southwestern U.S. mainly tied to a rising dry spell length trendThe extreme HYINT event is more likely to occur under anthropogenic forcing than natural forcing alone [ABSTRACT FROM AUTHOR]

Using a new modelling methodology known as "extreme event attribution," or EEA, climate scientists can now connect extreme weather to anthropogenic forcings. This paper seeks to uncover the significance of EEA for the epistemology of climate change, nature, and culture in the Anthropocene. First, we examine how EEA is emblematic of a larger turn in climate modelling, one that seeks to deploy anthropogenic climate change as an explanatory tool for an increasing number of socio‐natural phenomena. While some theorists have argued that the Anthropocene heralds the end of the nature–culture divide, we argue that EEA and similar modelling technologies seek to separate human influence from the natural variability of weather, thus establishing a new form of nature–culture divide mediated by computer simulation: a divide which we call "partitioned causality." Second, we demonstrate that partitioned causality is enabled by the relative hegemony of modelling technologies in climate change knowledge, as scientists retain substantial influence over who gets to "speak for" climate impacts. Finally, however, interviews with EEA scientists, journalists, and policymakers on the 2011–2017 California drought reveal that EEA remains a nascent scientific framework, one marked by epistemic slippage and divergent results. Thus, it serves as a powerful example of how emergent attempts to "domesticate" climate often become caught up in socio‐political conflicts around who – or what – has the power to shape discourses of climate change in the Anthropocene. This paper seeks to uncover the significance of extreme event attribution (EEA) for the epistemology of climate change, nature, and culture in the Anthropocene. We argue that, contrary to popular theories of the Anthropocene and its role in erasing the nature–culture divide, EEA and similar modelling technologies seek to separate human influence from the natural variability of weather, thus establishing a new form of nature–culture divide mediated by computer simulation – a divide we call "partitioned causality." We demonstrate that partitioned causality is enabled by the relative hegemony of modelling technologies in climate change knowledge, as scientists retain substantial influence over who gets to "speak for" climate impacts and influence. [ABSTRACT FROM AUTHOR]

Californian hydroclimate is strongly seasonal and prone to severe water shortages. Recent changes in climate trends have induced shifts in seasonality, thus exacerbating droughts, wildfires, and adverse water shortage effects on the environment and economy. Previous studies have examined the timing of the seasonal cycle shifts mainly as temperature driven earlier onset of the spring season. In this paper, we address quantitative changes in the onset, amounts, and termination of the precipitation season over the past 6 decades, as well as the large‐scale atmospheric circulation underpinning the seasonal cycle changes. We discover that the onset of the rainy season has been progressively delayed since the 1960s, and as a result the precipitation season has become shorter and sharper in California. The progressively later onset of the rainy season is shown to be related to the summer circulation pattern extending into autumn across the North Pacific, in particular, a delay in the strengthening of the Aleutian Low and later southward displacement of the North Pacific westerlies. Plain Language Summary: The rainy season over California is projected to show a distinct sharpening of the mean seasonal cycle, with winter precipitation increasing, and both autumn and spring precipitation decreasing. Our analysis of the past 6 decades of data for California suggests autumn decrease is already underway. A delayed start of the rainy season of 27 days since 1960s can exacerbate seasonal droughts and prolong the wildfire season. This delay occurs due to a number of conditions that controls precipitation: the summer circulation pattern has been extending throughout November across the North Pacific, and the wintertime strengthening of the Aleutian Low is delayed. Accordingly, the southward migration of the North Pacific jet stream as well as extratropical storm tracks are delayed, which marks the start of the California rainy season, are delayed. More work, using climate models, will be needed to provide a better understanding of atmospheric conditions across the North America and the North Pacific. However, our findings provide observational evidence for the projected rainfall change over California and inform ongoing discussion about the drying/wetting tendencies of the rainy season in California. Key Points: Rainfall data over the last 6 decades suggests a progressively delayed onset to the rainy season over CaliforniaA corresponding delay occurs in the transition from summer to wintertime circulation over the North PacificObserved autumn trends appear consistent with projected future shortening of the California rainy season [ABSTRACT FROM AUTHOR]

We combined two climate‐based distribution models with three finer‐scale suitability models to identify habitat for pronghorn recovery in California now and into the future. We used a consensus approach to identify areas of suitable climate now and future for pronghorn in California. We compared the results of climate models from two separate hypotheses about their historical ecology in the state. Under the migration hypothesis, pronghorn were expected to be limited climatically by extreme cold in winter and extreme heat in summer; under the niche reduction hypothesis, historical pronghorn of distribution would have better represented the climatic limitations of the species. We combined occurrences from GPS collars distributed across three populations of pronghorn in the state to create three distinct habitat suitability models: (1) an ensemble model using random forests, Maxent, classification and regression Trees, and a generalized linear model; (2) a step selection function; and (3) an expert‐driven model. We evaluated consensus among both the climate models and the suitability models to prioritize areas for, and evaluate the prospects of, pronghorn recovery. Climate suitability for pronghorn in the future depends heavily on model assumptions. Under the migration hypothesis, our model predicted that there will be no suitable climate in California in the future. Under the niche reduction hypothesis, by contrast, suitable climate will expand. Habitat suitability also depended on the methods used, but areas of consensus among all three models exist in large patches throughout the state. Identifying habitat for a species which has undergone extreme range collapse, and which has very fine scale habitat needs, presents novel challenges for spatial ecologists. Our multimethod, multihypothesis approach can allow habitat modelers to identify areas of consensus and, perhaps more importantly, fill critical knowledge gaps that could resolve disagreements among the models. For pronghorn, a better understanding of their upper thermal tolerances and whether historical populations migrated will be crucial to their potential recovery in California and throughout the arid Southwest. [ABSTRACT FROM AUTHOR]

We compare historical and end‐of‐century temperature and precipitation patterns over California from one dynamically downscaled simulation using the Weather Research and Forecast (WRF) model and two simulations statistically downscaled using Localized Constructed Analogs (LOCA). We uniquely separate causes of differences between dynamically and statistically based future climate projections into differences in historical climate (gridded observations versus regional climate model output) and differences in how these downscaling techniques explicitly handle future climate changes (numerical modeling versus analogs). In these methods, solutions between different downscaling techniques differ more in the future compared to the historical period. Changes projected by LOCA are insensitive to the choice of driving data. Only through dynamical downscaling can we simulate physically consistent regional springtime warming patterns across the Sierra Nevada, while the statistical simulations inherit an unphysical signal from their parent Global Climate Model (GCM) or gridded data. The results of our study clarify why these different techniques produce different outcomes and may also provide guidance on which downscaled products to use for certain impact analyses in California and perhaps other Mediterranean regimes. Plain Language Summary: Climate scientists commonly use global climate models (GCMs) to make end‐of‐century climate projections based on the primitive governing equations. Because of the limitations of current computing resources, GCMs cannot simulate and provide sufficiently high enough spatial resolution to capture the large geographic landscape and potentially diverse climate change patterns across California. As such, scientists typically apply two different approaches to generate high‐resolution regional climate projections: (1) feed coarse‐resolution GCM data into to higher‐resolution regional climate models (RCMs) to generate high‐resolution physically based climate change signals and (2) feed the GCM data into statistical models "trained" on historical observation data. In our study, we compare simulations using the former method with those using the latter method, examining April surface maximum temperature and annual precipitation projections. We find that the methods used here tend to yield similar projections if applied to a historical period (1990s), but their projections can be quite different for a future period (2090s), especially for precipitation. For our statistical methodology, the nature of projections is relatively insensitive to our choice of training data. Results here may help to guide others on methodology choice when projecting climate change across California or indeed other Mediterranean climate locales. Key Points: The climate change signal projected from the WRRF dynamical versus the LOCA statistical methods differsDifferences between the two downscaling methods are larger for future projections than for the historical periodLOCA statistical projections are relatively insensitive to training data [ABSTRACT FROM AUTHOR]

State and local governments, businesses, community organizations, and the general public are taking an increasingly significant role in climate impact assessment. Driven by impacts to constituents and customers and threats to human health, essential services, and property values, local organizations are on the front lines of responding to climate change. National and international efforts such as the U.S. National Climate Assessment and Intergovernmental Panel on Climate Change provide fundamental scientific understanding as well as methods and modeling tools. Subnational climate assessments can build on this foundation and tailor models and analyses to specific local or decision contexts. However, subnational climate assessment and adaptation presents new scientific and research challenges, such as those related to downscaling climate models, simulating extreme events, and understanding local values and institutional practices. As state, local, and sectoral assessments become more common across the nation, there is a critical opportunity to share learnings and identify challenges and pitfalls. Building off a National Academies of Sciences, Engineering, and Medicine activity focusing exclusively on subnational climate assessment, we consider the methods and results from a variety of examples to synthesize findings about current practices and the future of subnational climate assessment. Plain Language Summary: Climate assessments provide descriptions of the current and future impacts of climate change. Such assessments have long been a part of national and international activities on climate change. Increasingly, state and local governments, businesses, and other groups have begun climate assessments that address possible impacts from climate change specific to their constituents or customers. When taking national and international efforts to a local level, two important challenges arise. One is related to the challenge of applying science and modeling developed for the large scale to a smaller scale. The second is the need to translate technical analysis into usable information for decision makers and the public. Here we build off of a workshop convened by the National Academies of Sciences, Engineering, and Medicine to consider these issues. Key Points: State and local governments and others are taking an increasingly significant role in climate impact assessmentSuch assessments present analytical challenges from downscaling national models, simulating extremes, and integrating local perspectivesThe evolution of California's assessments, in its fourth iteration, reflects the demands for climate assessments to translate science into response activities [ABSTRACT FROM AUTHOR]

Recent fire seasons have fueled intense speculation regarding the effect of anthropogenic climate change on wildfire in western North America and especially in California. During 1972–2018, California experienced a fivefold increase in annual burned area, mainly due to more than an eightfold increase in summer forest‐fire extent. Increased summer forest‐fire area very likely occurred due to increased atmospheric aridity caused by warming. Since the early 1970s, warm‐season days warmed by approximately 1.4 °C as part of a centennial warming trend, significantly increasing the atmospheric vapor pressure deficit (VPD). These trends are consistent with anthropogenic trends simulated by climate models. The response of summer forest‐fire area to VPD is exponential, meaning that warming has grown increasingly impactful. Robust interannual relationships between VPD and summer forest‐fire area strongly suggest that nearly all of the increase in summer forest‐fire area during 1972–2018 was driven by increased VPD. Climate change effects on summer wildfire were less evident in nonforested lands. In fall, wind events and delayed onset of winter precipitation are the dominant promoters of wildfire. While these variables did not change much over the past century, background warming and consequent fuel drying is increasingly enhancing the potential for large fall wildfires. Among the many processes important to California's diverse fire regimes, warming‐driven fuel drying is the clearest link between anthropogenic climate change and increased California wildfire activity to date. Plain Language Summary: Since the early 1970s, California's annual wildfire extent increased fivefold, punctuated by extremely large and destructive wildfires in 2017 and 2018. This trend was mainly due to an eightfold increase in summertime forest‐fire area and was very likely driven by drying of fuels promoted by human‐induced warming. Warming effects were also apparent in the fall by enhancing the odds that fuels are dry when strong fall wind events occur. The ability of dry fuels to promote large fires is nonlinear, which has allowed warming to become increasingly impactful. Human‐caused warming has already significantly enhanced wildfire activity in California, particularly in the forests of the Sierra Nevada and North Coast, and will likely continue to do so in the coming decades. Key Points: Annual burned area in California increased fivefold during 1972–2018, mainly due to summer forest fireAnthropogenic warming very likely increased summer forest fire by drying fuels; this trend is likely to continueLarge fall fires are likely to become increasingly frequent with continued warming and possibly gradual declines in fall precipitation [ABSTRACT FROM AUTHOR]

The mountains of the Western United States provide a vital natural service through the storage and release of mountain snowpack, lessening impacts of seasonal aridity and satiating summer water demand. However, climate change continues to undermine these important processes. To understand how snowpack may change in the headwaters of California's major reservoirs, the North American Coordinated Regional Climate Downscaling Experiment is analyzed to assess peak water volume, peak timing, accumulation rate, melt rate, and snow season length across both latitudinal and elevational gradients. Under a high‐emissions scenario, end‐of‐century peak snowpack timing occurs 4 weeks earlier and peak water volume is 79.3% lower. The largest reductions are above Shasta, Oroville, and Folsom and between 0‐ and 2,000‐m elevations. Regional climate model and global forcing data set choice is important in determining historical snowpack character, yet by end century all models show a significant and similar decline in mountain snowpack. Plain Language Summary: Mountains are natural water towers that store snowpack in winter and release it as snowmelt during spring to summer. However, climate change has and continues to undermine this natural service. To answer where and when water resource management may be impacted by a future of low‐to‐no snowpack, we can leverage climate models, which are able to project the future conditions of mountain snowpack under various assumptions of global greenhouse gas emissions. In this study, we use five unique climate models under a high‐emissions scenario to evaluate a set of snowpack measures upstream of 10 California reservoirs. These 10 reservoirs represent nearly half of California's surface storage and by end century could face a 79% reduction in peak snowpack water volume. This work provides detailed guidance on the mountain snow conditions policymakers, water managers, and scientists will encounter in addressing adaptive resiliency in the face of climate change. Key Points: The historical role of California's Sierra Nevada mountain snowpack as a steady source of fresh water is fading due to climate changeAcross nine climate simulations historical snow representation varied but by end century agree on the magnitude and direction of declineUnder a high‐emissions scenario, a 79.3% decline in peak snow upstream of 40% of California's reservoir storage is shown by end century [ABSTRACT FROM AUTHOR]