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Abstract Extreme precipitation and consequent floods are some of California's most damaging natural disasters, but they are also critical to the state's water supply. This motivates the need to better understand the long‐term variability of these events across the region. This study examines the possibility of reconstructing extreme precipitation occurrences in the Sacramento River Watershed (SRW) of Northern California using a network of tree‐ring based moisture proxies across the Western US. We first develop a gridded reconstruction of the cold‐season standardized precipitation index (SPI) west of 100°W. We then develop an annual index of regional extreme precipitation occurrences in the SRW and use elastic net regression to relate that index to the gridded, tree‐ring based SPI. These regressions, built using SPI data across the SRW only and again across a broader region of the Western US, are used to develop reconstructions of interannual variability in extreme precipitation frequency back to 1400 CE. The SPI reconstruction is skillful across much of the West, including the Sacramento Valley and Central Oregon. The reconstructed SPI also captures historical interannual variations in extreme SRW precipitation, although individual events may be under‐ or over‐estimated. The reconstructions show more SRW extremes from 1580 to 1700 and 1850 to 1915, a dearth of extremes prior to 1550, and a 2–8 year oscillation after 1550. Using tree‐ring proxies beyond the SRW often dampens the reconstructed extremes, but these data occasionally help to identify known extreme events. Overall, reconstructions of SRW extreme precipitation can help to understand better the historic variability of these events. 

Abstract Weather regime based stochastic weather generators (WR‐SWGs) have recently been proposed as a tool to better understand multi‐sector vulnerability to deeply uncertain climate change. WR‐SWGs can distinguish and simulate different types of climate change that have varying degrees of uncertainty in future projections, including thermodynamic changes (e.g., rising temperatures, Clausius‐Clapeyron scaling of extreme precipitation) and dynamic changes (e.g., shifting circulation and storm tracks). These models require the accurate identification of WRs that are representative of both historical and plausible future patterns of atmospheric circulation, while preserving the complex space–time variability of weather processes. This study proposes a novel framework to identify such WRs based on WR‐SWG performance over a broad geographic area and applies this framework to a case study in California. We test two components of WR‐SWG design, including the method used for WR identification (Hidden Markov Models (HMMs) vs.K‐means clustering) and the number of WRs. For different combinations of these components, we assess performance of a multi‐site WR‐SWG using 14 metrics across 13 major California river basins during the cold season. Results show that performance is best using a small number of WRs (4–5) identified using an HMM. We then juxtapose the number of WRs selected based on WR‐SWG performance against the number of regimes identified using metastability analysis of atmospheric fields. Results show strong agreement in the number of regimes between the two approaches, suggesting that the use of metastable regimes could inform WR‐SWG design. We conclude with a discussion of the potential to expand this framework for additional WR‐SWG design parameters and spatial scales. 

Abstract Vulnerability‐based frameworks are increasingly used to better understand water system performance under climate change. This work advances the use of stochastic weather generators for climate vulnerability assessments that simulate weather based on patterns of regional atmospheric flow (i.e., weather regimes) conditioned on global‐scale climate features. The model is semiparametric by design and includes (1) a nonhomogeneous Markov chain for weather regime simulation; (2) block bootstrapping and a Gaussian copula for multivariate, multisite weather simulation; and (3) modules to impose thermodynamic and dynamical climate change, including Clausius‐Clapeyron precipitation scaling, elevation‐dependent warming, and shifting dynamics of the El Niño–Southern Oscillation (ENSO). In this way, the model can be used to evaluate climate impacts on water systems based on hypotheses of dynamic and thermodynamic climate change. The model is developed and tested for cold‐season climate in the Tuolumne River Basin in California but is broadly applicable across the western United States. Results show that eight weather regimes exert strong influences over local climate in the Tuolumne Basin. Model simulations adequately preserve many of the historical statistics for precipitation and temperature across sites, including the mean, variance, skew, and extreme values. Annual precipitation and temperature are somewhat underdispersed, and precipitation spell statistics are negatively biased by 1‐2 days. For simulations of future climate, the model can generate a range of Clausius‐Clapeyron scaling relationships and modes of elevation‐dependent warming. Model simulations also suggest a muted response of Tuolumne climate to changes in ENSO variability.