Internal variability in the climate system confounds assessment of human-induced climate change and imposes irreducible limits on the accuracy of climate change projections, especially at regional and decadal scales. A new collection of initial-condition large ensembles (LEs) generated with seven Earth system models under historical and future radiative forcing scenarios provides new insights into uncertainties due to internal variability versus model differences. These data enhance the assessment of climate change risks, including extreme events, and offer a powerful testbed for new methodologies aimed at separating forced signals from internal variability in the observational record. Opportunities and challenges confronting the design and dissemination of future LEs, including increased spatial resolution and model complexity alongside emerging Earth system applications, are discussed.

Surface weather conditions are closely governed by the large-scale 1 circulation of the atmosphere. Recent increases in the occurrence of some extreme 2 weather phenomena 1,2 have led to multiple mechanistic hypotheses linking changes 3 in atmospheric circulation to increasing extreme event probability 3-5 . However, 4 observed evidence of long-term change in atmospheric circulation remains 5 inconclusive 6-8 . Here we identify statistically significant trends in the occurrence of 6 mid-atmospheric circulation patterns, which partially explain observed trends in 7 surface temperature extremes over seven mid-latitude regions of the Northern 8Hemisphere. Utilizing self-organizing map (SOM) cluster analysis 9-12 , we detect 9 robust pattern trends in a subset of these regions during both the satellite 10 observation era Although most land regions show robust warming over the past century 13 , the 21 pattern of change has not been spatially uniform 14 . This heterogeneity results from 22 regional differences in the response of the climate system to increasing radiative forcing, 23and from the background noise of climate variability. Together, these factors 24 substantially increase the challenge of climate change detection, attribution, and 25 projection at regional and local scales 14,16 . 26The spatial pattern of changes in extreme weather events has generated arguments 27 that global warming has caused dynamic and/or thermodynamic changes that have 28 differentially altered extreme event probabilities 1,17 . Thermodynamic arguments are well 29 3 understood and observed. For example, the accumulation of heat in the atmosphere has 30 resulted in upward trends in hot extremes, downward trends in the majority of cold 31 extremes, and more intense hydroclimatic events 1,2 . Dynamic arguments have greater 32 uncertainties [15][16][17][18][19] . Changes in the large-scale atmospheric circulation -for instance, an 33 increase in the occurrence or persistence of high-amplitude wave patterns -could alter 34 the likelihood of extreme events 20 . Recent extremes in the Northern Hemisphere mid-35 latitudes 1,2,17 have motivated hypotheses of a dynamic linkage between "Arctic 36 Amplification", altered atmospheric circulation patterns, and changes in the probability of 37 mid-latitude extremes e.g., [3][4][5]17 . Despite divergent views on the causal direction of this 38 linkage 17 , altered atmospheric dynamics are consistently invoked. Although trends in 39 mean-seasonal mid-atmospheric geopotential height anomalies have been identified (Fig. 40 2.36 ref. 21; Fig. 1), evidence of changes in the occurrence of sub-seasonal atmospheric 41 patterns remains equivocal, as does their contribution to extreme event probabilities [6][7][8] . 42Previous efforts to detect trends in atmospheric circulation may have been 43 hampered by narrowly-defined, spatially-sensitive, and/or non-standardized metrics 3,[6][7][8]17 . 44We therefore employ a large-scale spatial characterization approach -Self-Organizing 45Map ("SOM") cluster an...

Efforts to understand the influence of historical global warming on individual extreme climate events have increased over the past decade. However, despite substantial progress, events that are unprecedented in the local observational record remain a persistent challenge. Leveraging observations and a large climate model ensemble, we quantify uncertainty in the influence of global warming on the severity and probability of the historically hottest month, hottest day, driest year, and wettest 5-d period for different areas of the globe. We find that historical warming has increased the severity and probability of the hottest month and hottest day of the year at >80% of the available observational area. Our framework also suggests that the historical climate forcing has increased the probability of the driest year and wettest 5-d period at 57% and 41% of the observed area, respectively, although we note important caveats. For the most protracted hot and dry events, the strongest and most widespread contributions of anthropogenic climate forcing occur in the tropics, including increases in probability of at least a factor of 4 for the hottest month and at least a factor of 2 for the driest year. We also demonstrate the ability of our framework to systematically evaluate the role of dynamic and thermodynamic factors such as atmospheric circulation patterns and atmospheric water vapor, and find extremely high statistical confidence that anthropogenic forcing increased the probability of record-low Arctic sea ice extent. event attribution | climate extremes | climate change | global warming T he last decade has witnessed increasing interest in possible connections between historical global warming and individual extreme climate events (1-9). This interest is grounded in both scientific and practical motivations. First, extremes underlie many of the most acute stresses on natural and human systems (10, 11). Understanding the influence of historical warming on extremes is therefore critical for detecting climate change impacts (12, 13). Second, trends in the frequency and/or intensity of extremes have already been detected (10, 11), implying increasing probability of events that are unprecedented in the observed record. Third, continued global warming is likely to cause widespread emergence of unprecedented events in the future (e.g., refs. 10 and 14).Effective management of climate-related risks therefore requires robust quantification of the probability of extremes in the current and future climate (10). For example, quantification of risk and liability (8,15), and design of resilient infrastructure and resource management systems (16), must account for both historical nonstationarity and the likelihood of future changes. Similarly, the United Nations mechanisms for climate change compensation, adaptation, and preparation create a practical need to quantify the contribution of historical emissions to individual extreme events (e.g., ref. 17). Finally, connections between historical warming and individual events have b...