The extratropical stratosphere in boreal winter is characterized by a strong circumpolar westerly jet, confining the coldest temperatures at high latitudes. The jet, referred to as the stratospheric polar vortex, is predominantly zonal and centered around the pole; however, it does exhibit large variability in wind speed and location. Previous studies showed that a weak stratospheric polar vortex can lead to cold-air outbreaks in the midlatitudes, but the exact relationships and mechanisms are unclear. Particularly, it is unclear whether stratospheric variability has contributed to the observed anomalous cooling trends in midlatitude Eurasia. Using hierarchical clustering, we show that over the last 37 years, the frequency of weak vortex states in mid- to late winter (January and February) has increased, which was accompanied by subsequent cold extremes in midlatitude Eurasia. For this region, 60% of the observed cooling in the era of Arctic amplification, that is, since 1990, can be explained by the increased frequency of weak stratospheric polar vortex states, a number that increases to almost 80% when El Niño–Southern Oscillation (ENSO) variability is included as well.

The Arctic is warming at a rate twice the global average and severe winter weather is reported to be increasing across many heavily populated mid-latitude regions, but there is no agreement on whether a physical link exists between the two phenomena. We use observational analysis to show that a lesser-known stratospheric polar vortex (SPV) disruption that involves wave reflection and stretching of the SPV is linked with extreme cold across parts of Asia and North America, including the recent February 2021 Texas cold wave, and has been increasing over the satellite era. We then use numerical modeling experiments forced with trends in autumn snow cover and Arctic sea ice to establish a physical link between Arctic change and SPV stretching and related surface impacts.

This study examines U.S. Northeast daily precipitation and extreme precipitation characteristics for the 1979–2008 period, focusing on daily station data. Seasonal and spatial distribution, time scale, and relation to large-scale factors are examined. Both parametric and nonparametric extreme definitions are considered, and the top 1% of wet days is chosen as a balance between sample size and emphasis on tail distribution. The seasonal cycle of daily precipitation exhibits two distinct subregions: inland stations characterized by frequent precipitation that peaks in summer and coastal stations characterized by less frequent but more intense precipitation that peaks in late spring as well as early fall. For both subregions, the frequency of extreme precipitation is greatest in the warm season, while the intensity of extreme precipitation shows no distinct seasonal cycle. The majority of Northeast precipitation occurs as isolated 1-day events, while most extreme precipitation occurs on a single day embedded in 2–5-day precipitation events. On these extreme days, examination of hourly data shows that 3 h or less account for approximately 50% of daily accumulation. Northeast station precipitation extremes are not particularly spatially cohesive: over 50% of extreme events occur at single stations only, and 90% occur at only 1–3 stations concurrently. The majority of extreme days (75%–100%) are related to extratropical storms, except during September, when more than 50% of extremes are related to tropical storms. Storm tracks on extreme days are farther southwest and more clustered than for all storm-related precipitation days.

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This paper surveys the current state of knowledge regarding large-scale meteorological patterns (LSMPs) associated with short-duration (less than 1 week) extreme precipitation events over North America. In contrast to teleconnections, which are typically defined based on the characteristic spatial variations of a meteorological field or on the remote circulation response to a known forcing, LSMPs are defined relative to the occurrence of a specific phenomenon-here, extreme precipitationand with an emphasis on the synoptic scales that have a primary influence in individual events, have medium-range weather predictability, and are well-resolved in both weather and climate models. For the LSMP relationship with extreme precipitation, we consider the previous literature with respect to definitions and data, dynamical mechanisms, model representation, and climate change trends. There is considerable uncertainty in identifying extremes based on existing observational precipitation data and some limitations in analyzing the associated LSMPs in reanalysis data. Many different definitions of "extreme" are in use, making it difficult to directly compare different studies. Dynamically, several types of meteorological systems-extratropical cyclones, tropical cyclones, mesoscale convective systems, and mesohighs-and several mechanisms-fronts, atmospheric rivers, and orographic ascent-have been shown to be important aspects of extreme precipitation LSMPs. The extreme precipitation is often realized through mesoscale processes organized, enhanced, or triggered by the LSMP. Understanding of model representation, trends, and projections for LSMPs is at an early stage, although some promising analysis techniques have been identified and the LSMP perspective is useful for evaluating the model dynamics associated with extremes.

The method of k-means cluster analysis is applied to U.S. wintertime daily 850-hPa winds across the Northeast. The resulting weather patterns are analyzed in terms of duration, station, gridded precipitation, storm tracks, and climate teleconnections. Five distinct weather patterns are identified. Weather type (WT) 1 is characterized by a ridge over the western Atlantic and positive precipitation anomalies as far north as the Great Lakes; WT2, by a trough along the eastern United States and positive precipitation anomalies into southern New England; WT3, by a trough over the western Atlantic and negative precipitation anomalies along much of the U.S. East Coast; WT4, by a trough east of Newfoundland and negative precipitation anomalies along parts of the U.S. East Coast; and WT5, by a broad, shallow trough over southeastern Canada and negative precipitation anomalies over the entire U.S. East Coast. WT5 and WT1 are the most persistent, while WT2 typically progresses quickly to WT3 and then to WT4. Based on mean station precipitation in the northeastern United States, most precipitation occurs in WT2 and WT3, with the least in WT1 and WT4. Extreme precipitation occurs most frequently in WT2. Storm tracks show that WT2 and WT3 are associated with coastal storms, while WT2 is also associated with Great Lakes storms. Teleconnections are linked with a change in WT frequency by more than a factor of 2 in several cases: for the North Atlantic Oscillation (NAO) in WT1 and WT4 and for the Pacific–North American (PNA) pattern in WT1 and WT3.

25Previous work has identified six Large-Scale Meteorological Patterns (LSMPs) of 26 dynamic tropopause height associated with extreme precipitation over the Northeast US, with 27 extreme precipitation defined as the top one percent of daily station precipitation. Here, we 28 examine the three-dimensional structure of the tropopause LSMPs in terms of circulation and 29 factors relevant to precipitation, including moisture, stability, and synoptic mechanisms 30 associated with lifting. Within each pattern, the link between the different factors and extreme 31 precipitation is further investigated by comparing the relative strength of the factors between 32 days with and without the occurrence of extreme precipitation. 33The six tropopause LSMPs include two ridge patterns, two eastern US troughs, and two 34 troughs centered over the Ohio Valley, with a strong seasonality associated with each pattern. 35Extreme precipitation in the ridge patterns is associated with both convective mechanisms 36 (instability combined with moisture transport from the Great Lakes and Western Atlantic) and 37 synoptic forcing related to Great Lakes storm tracks and embedded shortwaves. Extreme 38 precipitation associated with eastern US troughs involves intense southerly moisture transport 39 and strong quasi-geostrophic forcing of vertical velocity. Ohio Valley troughs are associated 40 with warm fronts and intense warm conveyor belts that deliver large amounts of moisture ahead 41 of storms, but little direct quasi-geostrophic forcing. Factors that show the largest difference 42 between days with and without extreme precipitation include integrated moisture transport, low-43 level moisture convergence, warm conveyor belts, and quasi-geostrophic forcing, with the 44 relative importance varying between patterns. 45

Sixteen Coupled Model Intercomparison Project Phase 6 (CMIP6) historical simulations (1950–2014) are compared to Northeast US observed precipitation and extreme precipitation-related synoptic circulation. A set of metrics based on the regional climate is used to assess how realistically the models simulate the observed distribution and seasonality of extreme precipitation, as well as the synoptic patterns associated with extreme precipitation. These patterns are determined by k-means typing of 500-hPa geopotential heights on extreme precipitation days (top 1% of days with precipitation). The metrics are formulated to evaluate the models’ extreme precipitation spatial variations, seasonal frequency, and intensity; and for circulation, the fit to observed patterns, pattern seasonality, and pattern location of extreme precipitation. Based on the metrics, the models vary considerably in their ability to simulate different aspects of regional precipitation, and a realistic simulation of the seasonality and distribution of precipitation does not necessarily correspond to a realistic simulation of the circulation patterns (reflecting the underlying dynamics of the precipitation), and vice versa. This highlights the importance of assessing both precipitation and its associated circulation. While the models vary in their ability to reproduce observed results, in general the higher resolution models score higher in terms of the metrics. Most models produce more frequent precipitation than that for observations, but capture the seasonality of precipitation intensity well, and capture at least several of the key characteristics of extreme precipitation-related circulation. These results do not appear to reflect a substantial improvement over a similar analysis of selected CMIP5 models.