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Abstract This study investigates seasonal precipitation trends across major watersheds in the continental United States over the past millennium (850 CE) and into the projected future (2100 CE). Using a non-stationary Standardized Precipitation Index (SPI) model, we quantify shifts in median precipitation climatology relative to an 1850 pre-Industrial baseline, integrating modern observations, tree-ring reconstructions, and climate model simulations. Trends at a gridded scale were then summarized by HUC-2 watershed to produce relevant results for water resources planning. Results show that northern and eastern watersheds have experienced wetting trends in the modern, Industrial era (post 1850), while southwestern regions have undergone intensifying drying trends, particularly during the summer. Trends vary seasonally, the most distinct north-south division occurring in winter and spring, while the region of increased drying expands to cover most of the U.S. during the summer. These trends are projected to continue on their current trajectory through the end of the century. By placing modern and projected precipitation trends into a multi-century historical context, we show that the magnitude of modern trends has exceeded pre-Industrial variability in many watersheds, suggesting the role of anthropogenic climate change in precipitation shifts. This study emphasizes the importance of adapting water management systems to ensure sustainable water availability under evolving climatic conditions. For example, the findings can inform water resources managers when reassessing water infrastructure and operations to mitigate the potential of increased flood risks in wetting regions and heightened water scarcity in drying areas. 

Numerous drought indices originate from the Standardized Precipitation Index (SPI) and use a moving-average structure to quantify drought severity by measuring normalized anomalies in hydroclimate variables. This study examines the theoretical probability of annual minima based on such a process. To accomplish this, we derive a stochastic model and use it to simulate 10 ×106 years of daily or monthly SPI values in order to determine the distribution of annual exceedance probabilities. We believe this is the first explicit quantification of annual extreme exceedances from a moving-average process where the moving-average window is proportionally large (5 %–200 %) relative to the year, as is the case for many moving-window drought indices. The resulting distribution of annual minima follows a generalized normal distribution rather than the generalized extreme-value (GEV) distribution, as would be expected from extreme-value theory. From a more applied perspective, this study provides the expected annual return periods for the SPI or related drought indices with common accumulation periods (moving-window length), ranging from 1 to 24 months. We show that the annual return period differs depending on both the accumulation period and the temporal resolution (daily or monthly). The likelihood of exceeding an SPI threshold in a given year decreases as the accumulation period increases. This study provides clarification and a caution for the use of annual return period terminology (e.g. the 100-year drought) with the SPI and a further caution for comparing annual exceedances across indices with different accumulation periods or resolutions. The study also distinguishes between theoretical values, as calculated here, and real-world exceedance probabilities, where there may be climatological autocorrelation beyond that created by the moving average. 

There are indications that the reference climatology underlying meteorological drought has shown nonstationarity at seasonal, decadal, and centennial timescales, impacting the calculation of drought indices and potentially having ecological and economic consequences. Analyzing these trends in meteorological drought climatology beyond 100 years, a time frame which exceeds the available period of observation data, contributes to a better understanding of the nonstationary changes, ultimately determining whether they are within the range of natural variability or outside this range. To accomplish this, our study introduces a novel approach to integrate unevenly scaled tree-ring proxy data from the North American Seasonal Precipitation Atlas (NASPA) with instrumental precipitation datasets by first temporally downscaling the proxy data to produce a regular time series and then modeling climate nonstationarity while simultaneously correcting model-induced bias. This new modeling approach was applied to 14 sites across the continental United States using the 3-month standardized precipitation index (SPI) as a basis. The findings showed that certain locations have experienced recent rapid shifts towards drier or wetter conditions during the instrumental period compared to the past 1000 years, with drying trends generally found in the west and wetting trends in the east. This study also found that seasonal shifts have occurred in some regions recently, with seasonality changes most notable for southern gauges. We expect that our new approach provides a foundation for incorporating various datasets to examine nonstationary variability in long-term precipitation climatology and to confirm the spatial patterns noted here in greater detail. 

Abstract Drought and pluvial extremes are defined as deviations from typical climatology; however, background climatology can shift over time in a non‐stationary climate, impacting interpretations of extremes. This study evaluated trends in meteorological drought and pluvial extremes by merging tree‐ring reconstructions, observations, and climate‐model simulations spanning 850–2100 CE across North America to determine whether modern and projected future precipitation lies outside the range of natural climate variability. Our results found widespread and spatially consistent exacerbation of drought and pluvial extremes, especially summer drought and winter pluvials, with drying in the west and south, wetting trends in the northeast, and intensification of both extremes across the east and north. Our study suggests that climate change has already shifted precipitation climatology beyond pre‐Industrial climatology and is projected to further intensify ongoing shifts. 

Abstract A large proportion of western North America experiences regular water stress, compounded by high seasonal and interannual variability. In the Intermountain West region, the El Niño/Southern Oscillation (ENSO) is a critical control on winter precipitation, but the nature of this signal is entangled with a combination of orographic effects and long-term climate trends. This study employs a spatially distributed, nonlinear spline model to isolate ENSO impacts from these other factors using gauge-based observations starting in 1871. In contrast to previous modelling approaches, our approach uses original gauge data, without shortening the record to accommodate a common period. This enables more detailed separation of ENSO effects from the confounding influence of topography and long-term trends, whereas the longer time frame permits more robust correlation with the ENSO signal. Here we show that the complex topography of the Intermountain West exaggerates the underlying ENSO signal, producing a 2.3–5.8 times increase in the range of ENSO-induced precipitation changes along high-elevation western slopes relative to lower elevations. ENSO effects on winter precipitation can be as large as ± 100 mm at high elevations. Further, our approach reveals that the previously recognized dipolar pattern of positive (negative) association of ENSO with precipitation in the south (north) manifests as an incremental relationship in the south but as a near-binary switch in effects between El Niño and La Niña in the north. The location and extent of the strongest precipitation differences vary during the positive and negative ENSO phases within each region. The intricacies of these spatial- and elevation-based modulations of ENSO impacts are especially informative for the northern centre of this dipole, where ENSO-precipitation relationships have previously been difficult to resolve. 

Abstract Analyzing gradual trends in meteorological drought has become increasingly important as anthropogenic climate change and natural climate variability interact to complicate measurement of drought severity. Complex seasonality and long-term trends pose a limitation in understanding spatial trends in nonstationary changes of meteorological drought in the United States. This study seeks to address this issue by simultaneously analyzing recurring seasonal patterns (stationary component) and long-term drought trends (nonstationary component), with a unique focus on nonlinear trends and common regional patterns. We analyzed 696 instrumental precipitation gauges with long historical records in the continental United States, using a novel spline-based model to disaggregate a 3-month meteorological drought index (SPI) into its seasonal and long-term components. The disaggregated components for each gauge were then clustered into subregions with similar seasonality and groupings with similar long-term trends using a two-step process. Our results identify clearly defined regions based on precipitation seasonality, while long-term trends are not spatially coherent with the seasonality. Instead, these findings support prior findings of an increasingly drier western United States and an increasingly wetter eastern United States over the last century, but with more nuanced spatial and temporal patterns. The new clustering analysis based on nonstationary meteorological drought trends can contribute to informing and adapting current water management strategies to long-term drought trends. Significance Statement This study considered 656 precipitation gauges across the continental United States to find regions with similar precipitation seasonality and then to group records with similar long-term climate trends. The study focused on 3-month average precipitation, a key indicator for drought monitoring. We identified eight regions across the United States with similar precipitation seasonality. From 1920 to the present, we found continuous drying trends throughout the western United States, continuously wetter trends in the northern plains, and an overall wetter trend interrupted by a midcentury dry period (1930–50) for much of the central Plains and Midwest. This study’s use of splines, or fitted curves, allowed these nonlinear patterns, which we believe better capture the nuances and intensification of climate change effects on precipitation. 

Abstract The standardized precipitation index (SPI) measures meteorological drought relative to historical climatology by normalizing accumulated precipitation. Longer record lengths improve parameter estimates, but these longer records may include signals of anthropogenic climate change and multidecadal natural climate fluctuations. Historically, climate nonstationarity has either been ignored or incorporated into the SPI using a quasi-stationary reference period, such as the WMO 30-yr period. This study introduces and evaluates a novel nonstationary SPI model based on Bayesian splines, designed to both improve parameter estimates for stationary climates and to explicitly incorporate nonstationarity. Using synthetically generated precipitation, this study directly compares the proposed Bayesian SPI model with existing SPI approaches based on maximum likelihood estimation for stationary and nonstationary climates. The proposed model not only reproduced the performance of existing SPI models but improved upon them in several key areas: reducing parameter uncertainty and noise, simultaneously modeling the likelihood of zero and positive precipitation, and capturing nonlinear trends and seasonal shifts across all parameters. Further, the fully Bayesian approach ensures all parameters have uncertainty estimates, including zero precipitation likelihood. The study notes that the zero precipitation parameter is too sensitive and could be improved in future iterations. The study concludes with an application of the proposed Bayesian nonstationary SPI model for nine gauges across a range of hydroclimate zones in the United States. Results of this experiment show that the model is stable and reproduces nonstationary patterns identified in prior studies, while also indicating new findings, particularly for the shape and zero precipitation parameters. Significance StatementWe typically measure how bad a drought is by comparing it with the historical record. With long-term changes in climate or other factors, however, a typical drought today may not have been typical in the recent past. The purpose of this study is to build a model that measures drought relative to a changing climate. Our results confirm that the model is accurate and captures previously noted climate change patterns—a drier western United States, a wetter eastern United States, earlier summer weather, and more extreme wet seasons. This is significant because this model can improve drought measurement and identify recent changes in drought.