![]() Almost all of these studies explore individual relationships between a single climate index and extreme precipitation furthermore, such analyses often compare years from high/positive phases of the index versus low/negative phases of the index (similar to the so-called “composite analysis” in Zhang et al. 2008), and the Artic Oscillation (AO Goswami et al. 2008), the Pacific North American pattern (PNA Archambault et al. 2001), the North Atlantic Oscillation (NAO Durkee et al. 1999 Gershunov and Cayan 2003 Cannon 2015), the Pacific Decadal Oscillation (PDO McCabe and Dettinger 1999), the Atlantic Multidecadal Oscillation (AMO Enfield et al. The literature contains a large number of studies that explore the relationship between climate variability and extreme precipitation, for example the El Niño–Southern Oscillation (ENSO Gershunov 1998 Cayan et al. Furthermore, a robust quantification of the natural variability in extreme precipitation from the observational record is relevant for improving seasonal and subseasonal predictability as well as evaluating climate models’ ability to capture these relationships. However, an important component of detecting trends and subsequently attributing them to anthropogenic climate change is an appropriate characterization of the natural variability inherent to extreme precipitation from the observational record. As such, there is a keen interest in attributing these trends to specific climate drivers, often anthropogenically-based (Min et al. ![]() 2016 Papalexiou and Montanari 2019), and this result has been verified in numerous studies over the contiguous United States (CONUS Kunkel 2003 Easterling et al. Globally, extreme precipitation in the observational record has been shown to contain nonstationarities over the past fifty to one hundred years (Hartmann et al. Furthermore, we specifically quantify how the spatial aspect of our analysis reduces uncertainty and increases detection of statistical significance while also discovering results that quantify the complex interconnected relationships between climate drivers and seasonal precipitation. ![]() We are able to detect at least some significant relationships in all seasons in spite of extremely large (> 95%) background variability in both mean and extreme precipitation. Across all climate indices considered, the signals are larger and can be detected more clearly for seasonal total versus extreme precipitation. In Part I of this work we focus on natural climate variability: detection is mostly limited to DJF and SON for the modes of variability considered, with the El Niño/Southern Oscillation, the Pacific–North American pattern, and the North Atlantic Oscillation exhibiting the largest influence. Our method utilizes a spatial statistical component that uses in situ measurements to resolve relationships to their native scales furthermore, we use a data-driven procedure to robustly determine statistical significance. An important aspect of our analysis is that we simultaneously isolate the individual effects of seven modes of variability while explicitly controlling for joint inter-mode relationships. In this work, we introduce a single framework for characterizing the historical signal (anthropogenic forcing) and noise (natural variability) in seasonal mean and extreme precipitation. While various studies explore the relationship between individual sources of climate variability and extreme precipitation, there is a need for improved understanding of how these physical phenomena simultaneously influence precipitation in the observational record across the contiguous United States. ![]()
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