Compound weather and climate events describe combinations of multiple climate drivers and/or hazards that contribute to societal or environmental risk. Although many climate-related disasters are caused by compound events, the understanding, analysis, quantification and prediction of such events is still in its infancy. In this Review, we propose a typology of compound events and suggest analytical and modelling approaches to aid in their investigation. We organize the highly diverse compound event types according to four themes: preconditioned, where a weather-driven or climate-driven precondition aggravates the impacts of a hazard; multivariate, where multiple drivers and/or hazards lead to an impact; temporally compounding, where a succession of hazards leads to an impact; and spatially compounding, where hazards in multiple connected locations cause an aggregated impact. Through structuring compound events and their respective analysis tools, the typology offers an opportunity for deeper insight into their mechanisms and impacts, benefiting the development of effective adaptation strategies. However, the complex nature of compound events results in some cases inevitably fitting into more than one class, necessitating soft boundaries within the typology. Future work must homogenize the available analytical approaches into a robust toolset for compound-event analysis under present and future climate conditions.
Compound events — a combination of multiple drivers and/or hazards that contribute to societal or environmental risk — are responsible for many of the most severe weather-related and climate-related impacts.
A classification of compound events is proposed, distinguishing events that are preconditioned, multivariate, temporally compounding and spatially compounding.
The typology aids compound-event analysis by facilitating the selection of appropriate analysis and modelling tools.
Through altering the distribution of climate variables and their spatial and temporal dependencies, climate change affects the likelihood, nature and impacts of compound events.
Bottom-up approaches, which link sectoral impacts to physical hazards, can help understand and, ultimately, better prepare for emerging risks posed by compound events.
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Zscheischler, J. et al. Future climate risk from compound events. Nat. Clim. Change 8, 469–477 (2018).
Kornhuber, K. et al. Amplified Rossby waves enhance risk of concurrent heatwaves in major breadbasket regions. Nat. Clim. Change 10, 48–53 (2020). Identified an atmospheric driver behind spatially concurrent hazards, an insight that is highly relevant for assessing the risk of global crop failure (spatially compounding).
Seneviratne, S. I. et al. in Managing the Risks of Extreme Events and Disasters to Advance Climate Change Adaptation (eds Field, C. B. et al.) 109–230 (Cambridge Univ. Press, 2012).
Benestad, R. E. & Haugen, J. E. On complex extremes: flood hazards and combined high spring-time precipitation and temperature in Norway. Clim. Change 85, 381–406 (2007).
Wahl, T., Jain, S., Bender, J., Meyers, S. D. & Luther, M. E. Increasing risk of compound flooding from storm surge and rainfall for major US cities. Nat. Clim. Change 5, 1093–1097 (2015).
Moftakhari, H. R., Salvadori, G., AghaKouchak, A., Sanders, B. F. & Matthew, R. A. Compounding effects of sea level rise and fluvial flooding. Proc. Natl Acad. Sci. USA 114, 9785–9790 (2017).
Hendry, A. et al. Assessing the characteristics and drivers of compound flooding events around the UK coast. Hydrol. Earth Syst. Sci. 23, 3117–3139 (2019). Quantifies the compound flooding risk associated with high skew surges and high river discharge across the UK, revealing the atmospheric drivers behind compound-flooding events (multivariate event).
van den Hurk, B., van Meijgaard, E., de Valk, P., van Heeringen, K.-J. & Gooijer, J. Analysis of a compounding surge and precipitation event in the Netherlands. Environ. Res. Lett. 10, 035001 (2015).
Leonard, M. et al. A compound event framework for understanding extreme impacts. Wiley Interdiscip. Rev. Clim. Change 5, 113–128 (2014). First introduced a framework for a systematic analysis of compound events.
Lian, J. J., Xu, K. & Ma, C. Joint impact of rainfall and tidal level on flood risk in a coastal city with a complex river network: a case study of Fuzhou City, China. Hydrol. Earth Syst. Sci. 17, 679–689 (2013).
Ward, P. J. et al. Dependence between high sea-level and high river discharge increases flood hazard in global deltas and estuaries. Environ. Res. Lett. 13, 084012 (2018).
Kjellstrom, T. et al. Heat, human performance, and occupational health: a key issue for the assessment of global climate change impacts. Annu. Rev. Public Health 37, 97–112 (2016).
Coffel, E. D., Horton, R. M. & de Sherbinin, A. Temperature and humidity based projections of a rapid rise in global heat stress exposure during the 21st century. Environ. Res. Lett. 13, 014001 (2018).
Raymond, C., Matthews, T. & Horton, R. M. The emergence of heat and humidity too severe for human tolerance. Sci. Adv. 6, eaaw1838 (2020).
Analitis, A. et al. Effects of heat waves on mortality: effect modification and confounding by air pollutants. Epidemiology 25, 15–22 (2014).
Holden, Z. A. et al. Decreasing fire season precipitation increased recent western US forest wildfire activity. Proc. Natl Acad. Sci. USA 115, E8349–E8357 (2018).
Raymond, C. et al. Understanding and managing connected extreme events. Nat. Clim. Change https://doi.org/10.1038/s41558-020-0790-4 (2020) Introduces the concept of ‘connected extreme events’, when the impacts of extreme weather and climate are amplified by physical interactions among events and across a complex set of societal factors.
Balch, J. K. et al. Social-environmental extremes: rethinking extraordinary events as outcomes of interacting biophysical and social systems. Earth’s Future https://agupubs.onlinelibrary.wiley.com/journal/23284277 (2020).
Tilloy, A., Malamud, B. D., Winter, H. & Joly-Laugel, A. A review of quantification methodologies for multi-hazard interrelationships. Earth-Sci. Rev. 196, 102881 (2019).
Berghuijs, W. R., Harrigan, S., Molnar, P., Slater, L. J. & Kirchner, J. W. The relative importance of different flood-generating mechanisms across Europe. Water Resour. Res. 55, 4582–4593 (2019). Illustrates that floods in Europe are rarely caused by rainfall extremes, but, rather, by snowmelt and by the concurrence of heavy precipitation with high antecedent soil moisture (preconditioned event).
Berghuijs, W. R., Woods, R. A., Hutton, C. J. & Sivapalan, M. Dominant flood generating mechanisms across the United States. Geophys. Res. Lett. 43, 4382–4390 (2016).
Martius, O. et al. The role of upper-level dynamics and surface processes for the Pakistan flood of July 2010. Q. J. R. Meteorol. Soc. 139, 1780–1797 (2013).
Grams, C. M., Binder, H., Pfahl, S., Piaget, N. & Wernli, H. Atmospheric processes triggering the central European floods in June 2013. Nat. Hazards Earth Syst. Sci. 14, 1691–1702 (2014).
Cohen, J., Ye, H. & Jones, J. Trends and variability in rain-on-snow events. Geophys. Res. Lett. 42, 7115–7122 (2015).
McCabe, G. J., Clark, M. P. & Hay, L. E. Rain-on-snow events in the western United States. Bull. Am. Meteorol. Soc. 88, 319–328 (2007).
Merz, R. & Blöschl, G. A process typology of regional floods. Water Resour. Res. 39, 1340 (2003).
Rössler, O. et al. Retrospective analysis of a nonforecasted rain-on-snow flood in the Alps-A matter of model limitations or unpredictable nature? Hydrol. Earth Syst. Sci. 18, 2265–2285 (2014).
Payne, A. E. et al. Responses and impacts of atmospheric rivers to climate change. Nat. Rev. Earth Environ. 1, 143–157 (2020).
Forkel, M. et al. Extreme fire events are related to previous-year surface moisture conditions in permafrost-underlain larch forests of Siberia. Environ. Res. Lett. 7, 044021 (2012).
Ruffault, J., Curt, T., Martin-Stpaul, N. K., Moron, V. & Trigo, R. M. Extreme wildfire events are linked to global-change-type droughts in the northern Mediterranean. Nat. Hazards Earth Syst. Sci. 18, 847–856 (2018).
Ren, D., Fu, R., Leslie, L. M. & Dickinson, R. E. Modeling the mudslide aftermath of the 2007 Southern California Wildfires. Nat. Hazards 57, 327–343 (2011).
Jacobs, L. et al. Reconstruction of a flash flood event through a multi-hazard approach: focus on the Rwenzori Mountains, Uganda. Nat. Hazards 84, 851–876 (2016).
Sippel, S. et al. Drought, heat, and the carbon cycle: a review. Curr. Clim. Chang. Rep. 4, 266–286 (2018).
Sippel, S. et al. Contrasting and interacting changes in simulated spring and summer carbon cycle extremes in European ecosystems. Environ. Res. Lett. 12, 075006 (2017).
Buermann, W. et al. Widespread seasonal compensation effects of spring warming on northern plant productivity. Nature 562, 110–114 (2018).
Marino, G. P., Kaiser, D. P., Gu, L. & Ricciuto, D. M. Reconstruction of false spring occurrences over the southeastern United States, 1901–2007: an increasing risk of spring freeze damage? Environ. Res. Lett. 6, 024015 (2011).
Hufkens, K. et al. Ecological impacts of a widespread frost event following early spring leaf-out. Glob. Chang. Biol. 18, 2365–2377 (2012).
Pfleiderer, P., Menke, I. & Schleussner, C.-F. Increasing risks of apple tree frost damage under climate change. Clim. Change 157, 515–525 (2019).
Rao, M. P. et al. Dzuds, droughts, and livestock mortality in Mongolia. Environ. Res. Lett. 10, 074012 (2015).
Liu, B., Siu, Y. L. & Mitchell, G. Hazard interaction analysis for multi-hazard risk assessment: A systematic classification based on hazard-forming environment. Nat. Hazards Earth Syst. Sci. 16, 629–642 (2016).
Mahony, C. R. & Cannon, A. J. Wetter summers can intensify departures from natural variability in a warming climate. Nat. Commun. 9, 783 (2018).
Flach, M. et al. Multivariate anomaly detection for Earth observations: a comparison of algorithms and feature extraction techniques. Earth Syst. Dyn. 8, 677–696 (2017).
Sadegh, M. et al. Multihazard scenarios for analysis of compound extreme events. Geophys. Res. Lett. 45, 5470–5480 (2018).
Bevacqua, E., Maraun, D., Hobæk Haff, I., Widmann, M. & Vrac, M. Multivariate statistical modelling of compound events via pair-copula constructions: analysis of floods in Ravenna (Italy). Hydrol. Earth Syst. Sci. 21, 2701–2723 (2017).
Zheng, F., Westra, S. & Sisson, S. A. Quantifying the dependence between extreme rainfall and storm surge in the coastal zone. J. Hydrol. 505, 172–187 (2013).
Wu, W. et al. Mapping dependence between extreme rainfall and storm surge. J. Geophys. Res. Ocean. 123, 2461–2474 (2018).
Bevacqua, E. et al. Higher probability of compound flooding from precipitation and storm surge in Europe under anthropogenic climate change. Sci. Adv. 5, eaaw5531 (2019).
Couasnon, A. et al. Measuring compound flood potential from river discharge and storm surge extremes at the global scale. Nat. Hazards Earth Syst. Sci. 20, 489–504 (2020).
Röthlisberger, M. & Martius, O. Quantifying the local effect of northern hemisphere atmospheric blocks on the persistence of summer hot and dry spells. Geophys. Res. Lett. 46, 10101–10111 (2019).
Berg, A. et al. Interannual coupling between summertime surface temperature and precipitation over land: processes and implications for climate change. J. Clim. 28, 1308–1328 (2015).
Schumacher, D. L. et al. Amplification of mega-heatwaves through heat torrents fuelled by upwind drought. Nat. Geosci. 12, 712–717 (2019).
Zscheischler, J. & Seneviratne, S. I. Dependence of drivers affects risks associated with compound events. Sci. Adv. 3, e1700263 (2017). Reports an increase in the dependence between summer temperature and precipitation with global warming, leading to an elevated risk of extremely hot and dry summers on top of long-term climate trends (multivariate event).
Hao, Z., Hao, F., Singh, V. P. & Zhang, X. Statistical prediction of the severity of compound dry-hot events based on El Niño-Southern Oscillation. J. Hydrol. 572, 243–250 (2019).
Cai, W. et al. Climate impacts of the El Niño–Southern oscillation on South America. Nat. Rev. Earth Environ. 1, 215–231 (2020).
Hoerling, M. et al. Anatomy of an extreme event. J. Clim. 26, 2811–2832 (2013).
Allen, C. D., Breshears, D. D. & McDowell, N. G. On underestimation of global vulnerability to tree mortality and forest die-off from hotter drought in the Anthropocene. Ecosphere 6, 1–55 (2015).
Goulden, M. L. & Bales, R. C. California forest die-off linked to multi-year deep soil drying in 2012–2015 drought. Nat. Geosci. 12, 632–637 (2019).
Coffel, E. D. et al. Future hot and dry years worsen Nile Basin water scarcity despite projected precipitation increases. Earths Future 7, 967–977 (2019).
Ciais, P. et al. Europe-wide reduction in primary productivity caused by the heat and drought in 2003. Nature 437, 529–533 (2005).
Zscheischler, J. et al. Carbon cycle extremes during the 21st century in CMIP5 models: future evolution and attribution to climatic drivers. Geophys. Res. Lett. 41, 8853–8861 (2014).
Zscheischler, J. et al. Impact of large-scale climate extremes on biospheric carbon fluxes: an intercomparison based on MsTMIP data. Glob. Biogeochem. Cycles 28, 585–600 (2014).
von Buttlar, J. et al. Impacts of droughts and extreme temperature events on gross primary production and ecosystem respiration: a systematic assessment across ecosystems and climate zones. Biogeosciences 15, 1293–1318 (2018).
Williams, A. P. & Abatzoglou, J. T. Recent advances and remaining uncertainties in resolving past and future climate effects on global fire activity. Curr. Clim. Chang. Rep. 2, 1–14 (2016).
Cook, M. A., King, C. W., Davidson, F. T. & Webber, M. E. Assessing the impacts of droughts and heat waves at thermoelectric power plants in the United States using integrated regression, thermodynamic, and climate models. Energy Rep. 1, 193–203 (2015).
Tschumi, E. & Zscheischler, J. Countrywide climate features during recorded climate-related disasters. Clim. Change 158, 593–609 (2020).
Otkin, J. A. et al. Flash droughts: a review and assessment of the challenges imposed by rapid-onset droughts in the United States. Bull. Am. Meteorol. Soc. 99, 911–919 (2018).
Stoffel, M. & Corona, C. Future winters glimpsed in the Alps. Nat. Geosci. 11, 458–460 (2018).
Martius, O., Pfahl, S. & Chevalier, C. A global quantification of compound precipitation and wind extremes. Geophys. Res. Lett. 43, 7709–7717 (2016).
Fink, A. H., Brücher, T., Ermert, V., Krüger, A. & Pinto, J. G. The European storm Kyrill in January 2007: synoptic evolution, meteorological impacts and some considerations with respect to climate change. Nat. Hazards Earth Syst. Sci. 9, 405–423 (2009).
Liberato, M. L. R. The 19 January 2013 windstorm over the North Atlantic: large-scale dynamics and impacts on Iberia. Weather Clim. Extremes 5, 16–28 (2014).
Raveh-Rubin, S. & Wernli, H. Large-scale wind and precipitation extremes in the Mediterranean: a climatological analysis for 1979–2012. Q. J. R. Meteorol. Soc. 141, 2404–2417 (2015).
Lin, N., Emanuel, K. A., Smith, J. A. & Vanmarcke, E. Risk assessment of hurricane storm surge for New York City. J. Geophys. Res. Atmos. 115, D18121 (2010).
Villarini, G., Vecchi, G. A. & Smith, J. A. Modeling the dependence of tropical storm counts in the North Atlantic basin on climate indices. Mon. Weather Rev. 138, 2681–2705 (2010).
Baldwin, J. W., Dessy, J. B., Vecchi, G. A. & Oppenheimer, M. Temporally compound heat wave events and global warming: an emerging hazard. Earths Future 7, 411–427 (2019).
Hughes, T. P. et al. Ecological memory modifies the cumulative impact of recurrent climate extremes. Nat. Clim. Change 9, 40–43 (2019).
Barton, Y. et al. Clustering of regional-scale extreme precipitation events in southern Switzerland. Mon. Weather Rev. 144, 347–369 (2016).
Wang, S. S.-Y. et al. Consecutive extreme flooding and heat wave in Japan: Are they becoming a norm? Atmos. Sci. Lett. 20, e933 (2019).
Matthews, T., Wilby, R. L. & Murphy, C. An emerging tropical cyclone–deadly heat compound hazard. Nat. Clim. Change 9, 602–606 (2019). Illustrates the risk of newly emerging compound events with global warming, in particular, a tropical cyclone followed by a deadly heatwave (temporally compounding).
Mailier, P. J., Stephenson, D. B., Ferro, C. A. T. & Hodges, K. I. Serial clustering of extratropical cyclones. Mon. Weather Rev. 134, 2224–2240 (2006). Models the serial clustering of extratropical cyclones with a point-process approach and links the strength of clustering with teleconnection indices (temporally compounding).
Pinto, J. G., Bellenbaum, N., Karremann, M. K. & Della-Marta, P. M. Serial clustering of extratropical cyclones over the North Atlantic and Europe under recent and future climate conditions. J. Geophys. Res. Atmos. 118, 12,476–12,485 (2013).
Priestley, M. D. K., Pinto, J. G., Dacre, H. F. & Shaffrey, L. C. The role of cyclone clustering during the stormy winter of 2013/2014. Weather 72, 187–192 (2017).
Vitolo, R., Stephenson, D. B., Cook, I. M. & Mitchell-Wallace, K. Serial clustering of intense European storms. Meteorol. Z. 18, 411–424 (2009).
Pinto, J. G. et al. Large-scale dynamics associated with clustering of extratropical cyclones affecting Western Europe. J. Geophys. Res. Atmos. 119, 13,704–13,719 (2014).
Priestley, M. D. K., Pinto, J. G., Dacre, H. F. & Shaffrey, L. C. Rossby wave breaking, the upper level jet, and serial clustering of extratropical cyclones in western Europe. Geophys. Res. Lett. 44, 514–521 (2017).
Mumby, P. J., Vitolo, R. & Stephenson, D. B. Temporal clustering of tropical cyclones and its ecosystem impacts. Proc. Natl Acad. Sci. USA 108, 17626–17630 (2011).
Villarini, G., Smith, J. A., Vitolo, R. & Stephenson, D. B. On the temporal clustering of US floods and its relationship to climate teleconnection patterns. Int. J. Climatol. 33, 629–640 (2013).
Gu, X., Zhang, Q., Singh, V. P., Chen, Y. D. & Shi, P. Temporal clustering of floods and impacts of climate indices in the Tarim River basin, China. Glob. Planet. Change 147, 12–24 (2016).
Mallakpour, I., Villarini, G., Jones, M. P. & Smith, J. A. On the use of Cox regression to examine the temporal clustering of flooding and heavy precipitation across the central United States. Glob. Planet. Change 155, 98–108 (2017).
Davies, H. C. Weather chains during the 2013/2014 winter and their significance for seasonal prediction. Nat. Geosci. 8, 833–837 (2015).
Fairman, T. A., Nitschke, C. R. & Bennett, L. T. Too much, too soon? A review of the effects of increasing wildfire frequency on tree mortality and regeneration in temperate eucalypt forests. Int. J. Wildland Fire 25, 831–848 (2016).
Ben-Ari, T. et al. Causes and implications of the unforeseen 2016 extreme yield loss in the breadbasket of France. Nat. Commun. 9, 1627 (2018). Reveals the drivers of the 2016 extreme wheat loss in France as a combination of unusually warm temperatures in late autumn and unusually wet conditions in the following spring (temporally compounding).
Steptoe, H., Jones, S. E. O. & Fox, H. Correlations between extreme atmospheric hazards and global teleconnections: implications for multihazard resilience. Rev. Geophys. 56, 50–78 (2018).
Anderson, W. B., Seager, R., Baethgen, W., Cane, M. & You, L. Synchronous crop failures and climate-forced production variability. Sci. Adv. 5, eaaw1976 (2019).
Singh, D. et al. Climate and the global famine of 1876–78. J. Clim. 31, 9445–9467 (2018).
Boers, N. et al. Complex networks reveal global pattern of extreme-rainfall teleconnections. Nature 566, 373–377 (2019).
Kornhuber, K. et al. Extreme weather events in early summer 2018 connected by a recurrent hemispheric wave-7 pattern. Environ. Res. Lett. 14, 054002 (2019).
Mehrabi, Z. & Ramankutty, N. Synchronized failure of global crop production. Nat. Ecol. Evol. 3, 780–786 (2019).
Lunt, T., Jones, A. W., Mulhern, W. S., Lezaks, D. P. M. & Jahn, M. M. Vulnerabilities to agricultural production shocks: an extreme, plausible scenario for assessment of risk for the insurance sector. Clim. Risk Manag. 13, 1–9 (2016).
Sun, X., Thyer, M., Renard, B. & Lang, M. A general regional frequency analysis framework for quantifying local-scale climate effects: A case study of ENSO effects on Southeast Queensland rainfall. J. Hydrol. 512, 53–68 (2014).
Lau, W. K. M. & Kim, K.-M. The 2010 Pakistan flood and Russian heat wave: teleconnection of hydrometeorological extremes. J. Hydrometeorol. 13, 392–403 (2012).
Wernli, H., Dirren, S., Liniger, M. A. & Zillig, M. Dynamical aspects of the life cycle of the winter storm ‘Lothar’ (24–26 December 1999). Q. J. R. Meteorol. Soc. 128, 405–429 (2002).
Guisado-Pintado, E. & Jackson, D. W. T. Multi-scale variability of storm Ophelia 2017: the importance of synchronised environmental variables in coastal impact. Sci. Total Environ. 630, 287–301 (2018).
van der Wiel, K. et al. Meteorological conditions leading to extreme low variable renewable energy production and extreme high energy shortfall. Renew. Sustain. Energy Rev. 111, 261–275 (2019).
Koks, E. E. et al. A global multi-hazard risk analysis of road and railway infrastructure assets. Nat. Commun. 10, 2677 (2019).
Haigh, I. D. et al. Spatial and temporal analysis of extreme sea level and storm surge events around the coastline of the UK. Sci. Data 3, 160107 (2016).
Mass, C. F. & Ovens, D. The Northern California wildfires of 8–9 October 2017: The role of a major downslope wind event. Bull. Am. Meteorol. Soc. 100, 235–256 (2019).
Vahedifard, F., AghaKouchak, A. & Jafari, N. H. Compound hazards yield Louisiana flood. Science 353, 1374 (2016).
Jongman, B. et al. Increasing stress on disaster-risk finance due to large floods. Nat. Clim. Change 4, 264–268 (2014).
Keef, C., Tawn, J. & Svensson, C. Spatial risk assessment for extreme river flows. J. R. Stat. Soc. Ser. C. Appl. Stat. 58, 601–618 (2009).
Quesada, B., Vautard, R., Yiou, P., Hirschi, M. & Seneviratne, S. I. Asymmetric European summer heat predictability from wet and dry southern winters and springs. Nat. Clim. Change 2, 736–741 (2012).
Ridder, N., de Vries, H. & Drijfhout, S. The role of atmospheric rivers in compound events consisting of heavy precipitation and high storm surges along the Dutch coast. Nat. Hazards Earth Syst. Sci. 18, 3311–3326 (2018).
Faranda, D., Messori, G. & Yiou, P. Diagnosing concurrent drivers of weather extremes: application to warm and cold days in North America. Clim. Dyn. 54, 2187–2201 (2020).
De Luca, P., Messori, G., Pons, F. M. E. & Faranda, D. Dynamical systems theory sheds new light on compound climate extremes in Europe and Eastern North America. Q. J. R. Meteorol. Soc. https://doi.org/10.1002/qj.3757 (2020).
Culley, S. et al. A bottom-up approach to identifying the maximum operational adaptive capacity of water resource systems to a changing climate. Water Resour. Res. 52, 6751–6768 (2016).
Prudhomme, C., Wilby, R. L., Crooks, S., Kay, A. L. & Reynard, N. S. Scenario-neutral approach to climate change impact studies: application to flood risk. J. Hydrol. 390, 198–209 (2010).
Zischg, A. P. et al. Effects of variability in probable maximum precipitation patterns on flood losses. Hydrol. Earth Syst. Sci. 22, 2759–2773 (2018).
Zscheischler, J. et al. A few extreme events dominate global interannual variability in gross primary production. Environ. Res. Lett. 9, 035001 (2014).
Zscheischler, J., Mahecha, M. D., Harmeling, S. & Reichstein, M. Detection and attribution of large spatiotemporal extreme events in Earth observation data. Ecol. Inform. 15, 66–73 (2013).
Hao, Z., Singh, V. & Hao, F. Compound extremes in hydroclimatology: a review. Water 10, 718 (2018).
Lloyd, E. A. & Shepherd, T. G. Environmental catastrophes, climate change, and attribution. Ann. N. Y. Acad. Sci. https://doi.org/10.1111/nyas.14308 (2020).
Sadegh, M., Ragno, E. & AghaKouchak, A. Multivariate Copula Analysis Toolbox (MvCAT): Describing dependence and underlying uncertainty using a Bayesian framework. Water Resour. Res. 53, 5166–5183 (2017).
Runge, J. et al. Inferring causation from time series in Earth system sciences. Nat. Commun. 10, 2553 (2019).
Croci-Maspoli, M. & Davies, H. C. Key dynamical features of the 2005/06 European winter. Mon. Weather Rev. 137, 664–678 (2009).
Schoelzel, C. & Friederichs, P. Multivariate non-normally distributed random variables in climate research–introduction to the copula approach. Nonlin. Process. Geophys. 15, 761–772 (2008).
Sarhadi, A., Burn, D. H., Concepción Ausín, M. & Wiper, M. P. Time-varying nonstationary multivariate risk analysis using a dynamic Bayesian copula. Water Resour. Res. 52, 2327–2349 (2016). Introduces an approach to model multivariate events in a non-stationary environment with copulas.
Sarhadi, A., Ausín, M. C., Wiper, M. P., Touma, D. & Diffenbaugh, N. S. Multidimensional risk in a nonstationary climate: Joint probability of increasingly severe warm and dry conditions. Sci. Adv. 4, eaau3487 (2018).
Kwon, H.-H. & Lall, U. A copula-based nonstationary frequency analysis for the 2012–2015 drought in California. Water Resour. Res. 52, 5662–5675 (2016).
Couasnon, A., Sebastian, A. & Morales-Nápoles, O. A copula-based Bayesian network for modeling compound flood hazard from riverine and coastal interactions at the catchment scale: An application to the Houston Ship Channel, Texas. Water 10, 1190 (2018).
Davison, A. C. & Huser, R. Statistics of extremes. Annu. Rev. Stat. Appl. 2, 203–235 (2015).
Hughes, J. P., Guttorp, P. & Charles, S. P. A non-homogeneous hidden Markov model for precipitation occurrence. J. R. Stat. Soc. Ser. C. Appl. Stat. 48, 15–30 (1999).
Davison, A. C., Padoan, S. A. & Ribatet, M. Statistical modeling of spatial extremes. Stat. Sci. 27, 161–186 (2012).
Touma, D., Michalak, A. M., Swain, D. L. & Diffenbaugh, N. S. Characterizing the spatial scales of extreme daily precipitation in the United States. J. Clim. 31, 8023–8037 (2018).
Blanchet, J. & Creutin, J. D. Co-occurrence of extreme daily rainfall in the French Mediterranean region. Water Resour. Res. 53, 9330–9349 (2017).
Le, P. D., Leonard, M. & Westra, S. Modeling spatial dependence of rainfall extremes across multiple durations. Water Resour. Res. 54, 2233–2248 (2018).
Vicente-Serrano, S. M. et al. A multiscalar global evaluation of the impact of ENSO on droughts. J. Geophys. Res. Atmos. 116, D20109 (2011).
Salvadori, G., Durante, F., De Michele, C., Bernardi, M. & Petrella, L. A multivariate copula-based framework for dealing with hazard scenarios and failure probabilities. Water Resour. Res. 52, 3701–3721 (2016).
Gouldby, B. et al. Multivariate extreme value modelling of sea conditions around the coast of England. Proc. Inst. Civ. Eng. Marit. Eng. 170, 3–20 (2017).
Poschlod, B., Zscheischler, J., Sillmann, J., Wood, R. R. & Ludwig, R. Climate change effects on hydrometeorological compound events over southern Norway. Weather Clim. Extremes 28, 100253 (2020).
Zscheischler, J., Fischer, E. M. & Lange, S. The effect of univariate bias adjustment on multivariate hazard estimates. Earth Syst. Dyn. 10, 31–43 (2019).
Shepherd, T. G. et al. Storylines: an alternative approach to representing uncertainty in physical aspects of climate change. Clim. Change 151, 555–571 (2018).
Intergovernmental Panel on Climate Change (IPCC). Climate Change 2013: the Physical Science Basis. Working Group I Contribution to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change (Cambridge Univ. Press, 2013).
Little, C. M. et al. Joint projections of US East Coast sea level and storm surge. Nat. Clim. Change 5, 1114–1120 (2015).
Mazdiyasni, O. & AghaKouchak, A. Substantial increase in concurrent droughts and heatwaves in the United States. Proc. Natl Acad. Sci. USA 112, 11484–11489 (2015).
Manning, C. et al. Increased probability of compound long-duration dry and hot events in Europe during summer (1950–2013). Environ. Res. Lett. 14, 094006 (2019).
Sharma, S. & Mujumdar, P. Increasing frequency and spatial extent of concurrent meteorological droughts and heatwaves in India. Sci. Rep. 7, 15582 (2017).
Skinner, C. B., Poulsen, C. J. & Mankin, J. S. Amplification of heat extremes by plant CO2 physiological forcing. Nat. Commun. 9, 1094 (2018).
Lemordant, L. & Gentine, P. Vegetation response to rising CO2 impacts extreme temperatures. Geophys. Res. Lett. 46, 1383–1392 (2019).
Swann, A. L. S. Plants and drought in a changing climate. Curr. Clim. Change Rep. 4, 192–201 (2018).
Mankin, J. S. et al. Blue water trade-offs with vegetation in a CO2-enriched climate. Geophys. Res. Lett. 45, 3115–3125 (2018).
Piao, S. et al. Characteristics, drivers and feedbacks of global greening. Nat. Rev. Earth Environ. 1, 14–27 (2020).
Swain, D. L., Langenbrunner, B., Neelin, J. D. & Hall, A. Increasing precipitation volatility in twenty-first-century California. Nat. Clim. Change 8, 427–433 (2018).
Williams, A. P. et al. Observed impacts of anthropogenic climate change on wildfire in California. Earths Future 7, 892–910 (2019).
Tigchelaar, M., Battisti, D. S., Naylor, R. L. & Ray, D. K. Future warming increases probability of globally synchronized maize production shocks. Proc. Natl Acad. Sci. USA 115, 6644–6649 (2018).
Yang, H. et al. Strong but intermittent spatial covariations in tropical land temperature. Geophys. Res. Lett. 46, 356–364 (2019).
Gaupp, F., Hall, J., Hochrainer-Stigler, S. & Dadson, S. Changing risks of simultaneous global breadbasket failure. Nat. Clim. Change 10, 54–57 (2020).
Berghuijs, W. R., Allen, S. T., Harrigan, S. & Kirchner, J. W. Growing spatial scales of synchronous river flooding in Europe. Geophys. Res. Lett. 46, 1423–1428 (2019).
Haarsma, R. J. et al. More hurricanes to hit western Europe due to global warming. Geophys. Res. Lett. 40, 1783–1788 (2013).
Bougeault, P. et al. The THORPEX interactive grand global ensemble. Bull. Am. Meteorol. Soc. 91, 1059–1072 (2010).
Deser, C. et al. Insights from earth system model initial-condition large ensembles and future prospects. Nat. Clim. Change 10, 277–286 (2020).
Knippertz, P. & Wernli, H. A Lagrangian climatology of tropical moisture exports to the Northern Hemispheric extratropics. J. Clim. 23, 987–1003 (2010).
The authors acknowledge the European COST Action DAMOCLES (CA17109). J.Z. acknowledges financial support from the Swiss National Science Foundation (Ambizione grant 179876). O.M. acknowledges support from the Swiss National Science Foundation (grant no. 178751). A portion of C.R.’s work was carried out at the Jet Propulsion Laboratory, California Institute of Technology, under a contract with the National Aeronautics and Space Administration. E.B. acknowledges financial support from the European Research Council grant ACRCC (project 339390). A.M.R. was supported by the Scientific Employment Stimulus 2017 from the Fundação para a Ciência e a Tecnologia, Portugal (FCT, CEECIND/00027/2017). N.N.R. was funded by the Australian Research Council Centre of Excellence for Climate Extremes (CE170100023). This work contributes to the World Climate Research Programme (WCRP) Grand Challenge on Weather and Climate Extremes.
The authors declare no competing interests.
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Zscheischler, J., Martius, O., Westra, S. et al. A typology of compound weather and climate events. Nat Rev Earth Environ 1, 333–347 (2020). https://doi.org/10.1038/s43017-020-0060-z
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