Rogers, Cassandra D.W.Kornhuber, KaiPerkins-Kirkpatrick, Sarah E.Loikith, Paul C.Singh, Deepti2025-06-032025-06-030894-8755ORCID:/0000-0001-9443-4915/work/171154962http://www.scopus.com/inward/record.url?scp=85123421869&partnerID=8YFLogxKhttps://hdl.handle.net/1885/733756537Simultaneous heatwaves affecting multiple regions (referred to as concurrent heatwaves) pose compounding threats to various natural and societal systems, including global food chains, emergency response systems, and reinsurance industries. While anthropogenic climate change is increasing heatwave risks across most regions, the interactions between warming and circulation changes that yield concurrent heatwaves remain understudied. Here, we quantify historical (1979-2019) trends in concurrent heatwaves during the warm season [May-September (MJJAS)] across the Northern Hemisphere mid- to high latitudes. We find a significant increase of ∼46% in the mean spatial extent of concurrent heatwaves and ∼17% increase in their maximum intensity, and an approximately sixfold increase in their frequency. Using self-organizing maps, we identify large-scale circulation patterns (300 hPa) associated with specific concurrent heatwave configurations across Northern Hemisphere regions. We show that observed changes in the frequency of specific circulation patterns preferentially increase the risk of concurrent heatwaves across particular regions. Patterns linking concurrent heatwaves across eastern North America, eastern and northern Europe, parts of Asia, and the Barents and Kara Seas show the largest increases in frequency (∼5.9 additional days per decade). We also quantify the relative contributions of circulation pattern changes and warming to overall observed concurrent heatwave day frequency trends. While warming has a predominant and positive influence on increasing concurrent heatwave frequency, circulation pattern changes have a varying influence and account for up to 0.8 additional concurrent heatwave days per decade. Identifying regions with an elevated risk of concurrent heatwaves and understanding their drivers is indispensable for evaluating projected climate risks on interconnected societal systems and fostering regional preparedness in a changing climate.Acknowledgments. The authors thank Peter Gibson, Dmitri Kalashnikov, and Melissa Gervais for their insightful discussions about SOMs, which assisted in the development of this paper’s methods. C.D.W.R. and D.S. are supported by Washington State University and the U.S. National Science Foundation through Grant AGS-1934383. K.K. is supported by the U.S. National Science Foundation through Grant AGS-1934358. S.E.P.-K. is supported by Australian Research Council Grant FT170100106. P.C.L. is supported by the U.S. National Science Foundation through Grant AGS-1621554. The majority of the analyses for this project were run on Washington State University’s high-performance computing cluster, Kamiak. Data analyses are performed using Matlab, Python, and CDO. The following Matlab packages were utilized for our research: SOM Toolbox (Laboratory of Computer and Information Science 2009), the Climate Data Toolbox for Matlab (Greene et al. 2019), and the Mann–Kendall test package (Fatichi 2020). The authors declare no competing interests. C.D.W.R. and D.S. conceived and designed the study with feedback from all authors. C.D.W.R. performed all analysis and lead the study. C.D.W.R. and D.S. wrote the paper with contributions from K.K., S.E.P.-K., and P.C.L.16enPublisher Copyright: © 2022 American Meteorological Society. All rights reserved.AnomaliesAtmosphereAtmospheric circulationClimate changeClimate variabilityClusteringDynamicsExtreme eventsHeatingNeural networksTemperatureThermodynamicsTrends2022-01-1910.1175/JCLI-D-21-0200.185123421869