Articles | Volume 25, issue 9
https://doi.org/10.5194/nhess-25-3221-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/nhess-25-3221-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Extreme heat and mortality in the state of Rio de Janeiro in November 2023: attribution to climate change and ENSO
Departamento de Física de la Tierra y Astrofísica, Facultad de Ciencias Físicas, Universidad Complutense de Madrid (UCM), Plaza de las Ciencias 1, 28040 Madrid, Spain
Instituto de Geociencias (IGEO), Consejo Superior de Investigaciones Científicas–Universidad Complutense de Madrid (CSIC–UCM), 28040 Madrid, Spain
Departamento de Ciencias de la Atmósfera y los Océanos, Facultad de Ciencias Exactas y Naturales, Universidad de Buenos Aires (FCEN, UBA), Buenos Aires, Argentina
David Barriopedro
Instituto de Geociencias (IGEO), Consejo Superior de Investigaciones Científicas–Universidad Complutense de Madrid (CSIC–UCM), 28040 Madrid, Spain
Ricardo García-Herrera
Departamento de Física de la Tierra y Astrofísica, Facultad de Ciencias Físicas, Universidad Complutense de Madrid (UCM), Plaza de las Ciencias 1, 28040 Madrid, Spain
Instituto de Geociencias (IGEO), Consejo Superior de Investigaciones Científicas–Universidad Complutense de Madrid (CSIC–UCM), 28040 Madrid, Spain
Santiago Beguería
Estación Experimental de Aula Dei–Consejo Superior de Investigaciones Científicas (EEAD–CSIC), 50059 Zaragoza, Spain
Related authors
Solange Suli, David Barriopedro, Ricardo García-Herrera, Soledad Collazo, Antonello Squintu, and Matilde Rusticucci
EGUsphere, https://doi.org/10.5194/egusphere-2025-3357, https://doi.org/10.5194/egusphere-2025-3357, 2025
This preprint is open for discussion and under review for Weather and Climate Dynamics (WCD).
Short summary
Short summary
Heat extremes are becoming more frequent and intense in southern South America. This study uses a storyline approach to explore how different climate drivers shape future summer temperature extremes. Using climate model simulations, we identified key drivers, such as soil moisture, sea surface temperature, and atmospheric circulation, to build physically consistent scenarios that explain much of the uncertainty in regional warming projections.
Solange Suli, David Barriopedro, Ricardo García-Herrera, Soledad Collazo, Antonello Squintu, and Matilde Rusticucci
EGUsphere, https://doi.org/10.5194/egusphere-2025-3357, https://doi.org/10.5194/egusphere-2025-3357, 2025
This preprint is open for discussion and under review for Weather and Climate Dynamics (WCD).
Short summary
Short summary
Heat extremes are becoming more frequent and intense in southern South America. This study uses a storyline approach to explore how different climate drivers shape future summer temperature extremes. Using climate model simulations, we identified key drivers, such as soil moisture, sea surface temperature, and atmospheric circulation, to build physically consistent scenarios that explain much of the uncertainty in regional warming projections.
Magí Franquesa, Fergus Reig, Manuel Arretxea, Maria Adell-Michavila, Amar Halifa-Marín, Daniel Vilas, Santiago Beguería, and Sergio Martín Vicente-Serrano
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2024-351, https://doi.org/10.5194/essd-2024-351, 2025
Preprint under review for ESSD
Short summary
Short summary
Our study created a unique database that tracks vegetation health across Spain and the Balearic Islands from 1981 to now, updated every two weeks. By using satellite images from multiple sources, we provide accurate and consistent data that helps detect changes in vegetation due to factors like fires. This tool is crucial for farmers, environmental managers, and policymakers to monitor and protect plant life, ensuring better management of natural resources and agricultural productivity.
Chaim I. Garfinkel, Zachary D. Lawrence, Amy H. Butler, Etienne Dunn-Sigouin, Irene Erner, Alexey Y. Karpechko, Gerbrand Koren, Marta Abalos, Blanca Ayarzagüena, David Barriopedro, Natalia Calvo, Alvaro de la Cámara, Andrew Charlton-Perez, Judah Cohen, Daniela I. V. Domeisen, Javier García-Serrano, Neil P. Hindley, Martin Jucker, Hera Kim, Robert W. Lee, Simon H. Lee, Marisol Osman, Froila M. Palmeiro, Inna Polichtchouk, Jian Rao, Jadwiga H. Richter, Chen Schwartz, Seok-Woo Son, Masakazu Taguchi, Nicholas L. Tyrrell, Corwin J. Wright, and Rachel W.-Y. Wu
Weather Clim. Dynam., 6, 171–195, https://doi.org/10.5194/wcd-6-171-2025, https://doi.org/10.5194/wcd-6-171-2025, 2025
Short summary
Short summary
Variability in the extratropical stratosphere and troposphere is coupled, and because of the longer timescales characteristic of the stratosphere, this allows for a window of opportunity for surface prediction. This paper assesses whether models used for operational prediction capture these coupling processes accurately. We find that most processes are too weak; however downward coupling from the lower stratosphere to the near surface is too strong.
Emilio Cuevas-Agulló, David Barriopedro, Rosa Delia García, Silvia Alonso-Pérez, Juan Jesús González-Alemán, Ernest Werner, David Suárez, Juan José Bustos, Gerardo García-Castrillo, Omaira García, África Barreto, and Sara Basart
Atmos. Chem. Phys., 24, 4083–4104, https://doi.org/10.5194/acp-24-4083-2024, https://doi.org/10.5194/acp-24-4083-2024, 2024
Short summary
Short summary
During February–March (FM) 2020–2022, unusually intense dust storms from northern Africa hit the western Euro-Mediterranean (WEM). Using dust products from satellites and atmospheric reanalysis for 2003–2022, results show that cut-off lows and European blocking are key drivers of FM dust intrusions over the WEM. A higher frequency of cut-off lows associated with subtropical ridges is observed in the late 2020–2022 period.
Santiago Beguería, Dhais Peña-Angulo, Víctor Trullenque-Blanco, and Carlos González-Hidalgo
Earth Syst. Sci. Data, 15, 2547–2575, https://doi.org/10.5194/essd-15-2547-2023, https://doi.org/10.5194/essd-15-2547-2023, 2023
Short summary
Short summary
A gridded dataset on monthly precipitation over mainland Spain between spans 1916–2020. The dataset combines ground observations from the Spanish National Climate Data Bank and new data rescued from meteorological yearbooks published prior to 1951, which almost doubled the number of weather stations available during the first decades of the 20th century. Geostatistical techniques were used to interpolate a regular 10 x 10 km grid.
Zachary D. Lawrence, Marta Abalos, Blanca Ayarzagüena, David Barriopedro, Amy H. Butler, Natalia Calvo, Alvaro de la Cámara, Andrew Charlton-Perez, Daniela I. V. Domeisen, Etienne Dunn-Sigouin, Javier García-Serrano, Chaim I. Garfinkel, Neil P. Hindley, Liwei Jia, Martin Jucker, Alexey Y. Karpechko, Hera Kim, Andrea L. Lang, Simon H. Lee, Pu Lin, Marisol Osman, Froila M. Palmeiro, Judith Perlwitz, Inna Polichtchouk, Jadwiga H. Richter, Chen Schwartz, Seok-Woo Son, Irene Erner, Masakazu Taguchi, Nicholas L. Tyrrell, Corwin J. Wright, and Rachel W.-Y. Wu
Weather Clim. Dynam., 3, 977–1001, https://doi.org/10.5194/wcd-3-977-2022, https://doi.org/10.5194/wcd-3-977-2022, 2022
Short summary
Short summary
Forecast models that are used to predict weather often struggle to represent the Earth’s stratosphere. This may impact their ability to predict surface weather weeks in advance, on subseasonal-to-seasonal (S2S) timescales. We use data from many S2S forecast systems to characterize and compare the stratospheric biases present in such forecast models. These models have many similar stratospheric biases, but they tend to be worse in systems with low model tops located within the stratosphere.
Piero Lionello, David Barriopedro, Christian Ferrarin, Robert J. Nicholls, Mirko Orlić, Fabio Raicich, Marco Reale, Georg Umgiesser, Michalis Vousdoukas, and Davide Zanchettin
Nat. Hazards Earth Syst. Sci., 21, 2705–2731, https://doi.org/10.5194/nhess-21-2705-2021, https://doi.org/10.5194/nhess-21-2705-2021, 2021
Short summary
Short summary
In this review we describe the factors leading to the extreme water heights producing the floods of Venice. We discuss the different contributions, their relative importance, and the resulting compound events. We highlight the role of relative sea level rise and the observed past and very likely future increase in extreme water heights, showing that they might be up to 160 % higher at the end of the 21st century than presently.
Jacob W. Maddison, Marta Abalos, David Barriopedro, Ricardo García-Herrera, Jose M. Garrido-Perez, and Carlos Ordóñez
Weather Clim. Dynam., 2, 675–694, https://doi.org/10.5194/wcd-2-675-2021, https://doi.org/10.5194/wcd-2-675-2021, 2021
Short summary
Short summary
Air stagnation occurs when an air mass becomes settled over a region and precipitation is suppressed. Pollutant levels can rise during stagnation. The synoptic- to large-scale influence on European air stagnation and pollution is explored here. We show that around 60 % of the monthly variability in air stagnation and pollutants can be explained by dynamical indices describing the atmospheric circulation. The weather systems most related to stagnation are different for regions across Europe.
Cited articles
de Abreu, R. C., Tett, S. F. B., Schurer, A., and Rocha, H. R.: Attribution of Detected Temperature Trends in Southeast Brazil, Geophys. Res. Lett., 46, 8407–8414, https://doi.org/10.1029/2019GL083003, 2019.
Akaike, H.: Information theory and an extension of the maximum likelihood principle, edited by: Parzen, E., Tanabe, K., and Kitagawa, G.: Selected Papers of Hirotugu Akaike, Springer, 199–213, ISBN 978-0-387-98355-4, 1998.
Allen, M.: Liability for climate change, Nature, 421, 891–892, https://doi.org/10.1038/421891a, 2003.
Alvarez, M. S., Vera, C. S., Kiladis, G. N., and Liebmann, B.: Influence of the Madden Julian Oscillation on precipitation and surface air temperature in South America, Clim. Dynam., 46, 245–262, https://doi.org/10.1007/s00382-015-2581-6, 2016.
Arreyndip, N. A.: Identifying agricultural disaster risk zones for future climate actions, PLoS One, 16, e0260430, https://doi.org/10.1371/journal.pone.0260430, 2021.
Avelar, S. and Tokarczyk, P.: Analysis of land use and land cover change in a coastal area of Rio de Janeiro using high-resolution remotely sensed data, J. Appl. Remote Sens., 8, 083631, https://doi.org/10.1117/1.JRS.8.083631, 2014.
Avila-Diaz, A., Benezoli, V., Justino, F., Torres, R., and Wilson, A.: Assessing current and future trends of climate extremes across Brazil based on reanalyses and earth system model projections, Clim. Dynam., 55, 1403–1426, https://doi.org/10.1007/s00382-020-05333-z, 2020.
Ballester, J., Quijal-Zamorano, M., Méndez Turrubiates, R. F., Pegenaute, F., Herrmann, F. R., Robine, J. M., Basagaña, X., Tonne, C., Antó, J. M., and Achebak, H.: Heat-related mortality in Europe during the summer of 2022, Nat. Med., 29, 1857–1866, https://doi.org/10.1038/s41591-023-02419-z, 2023.
Balmaceda-Huarte, R., Olmo, M. E., Bettolli, M. L., and Poggi, M. M.: Evaluation of multiple reanalyses in reproducing the spatio-temporal variability of temperature and precipitation indices over southern South America, Int. J. Climatol., 41, 5572–5595, https://doi.org/10.1002/joc.7142, 2021.
de Barros Soares, D., Lee, H., Loikith, P. C., Barkhordarian, A., and Mechoso, C. R.: Can significant trends be detected in surface air temperature and precipitation over South America in recent decades?, Int. J. Climatol., 37, 1483–1493, https://doi.org/10.1002/joc.4792, 2017.
Beguería, S., Tomas-Burguera, M., Serrano-Notivoli, R., Peña-Angulo, D., Vicente-Serrano, S. M., and González-Hidalgo, J.-C.: Gap Filling of Monthly Temperature Data and Its Effect on Climatic Variability and Trends, J. Climate, 32, 7797–7821, https://doi.org/10.1175/JCLI-D-19-0244.1, 2019.
Beguería, S., Tomas-Burguera, M., Serrano-Notivoli, R., Barriopedro, D., and Vicente-Serrano, S. M.: Evolution of Extreme Precipitation in Spain: Contribution of Atmospheric Dynamics and Long-Term Trends, https://doi.org/10.2139/ssrn.4450703, 2023.
Belzile, L. R., Dutang, C., Northrop, P. J., and Opitz, T.: A modeler's guide to extreme value software, https://doi.org/10.1007/s10687-023-00475-9, 2023.
Bitencourt, D. P., Muniz Alves, L., Shibuya, E. K., de Ângelo da Cunha, I., and Estevam de Souza, J. P.: Climate change impacts on heat stress in Brazil – Past, present, and future implications for occupational heat exposure, Int. J. Climatol., 41, https://doi.org/10.1002/joc.6877, 2021.
Brazilian National Institute of Meteorology (INMET): Temperature in the State of Rio de Janeiro, INMET [data set], https://bdmep.inmet.gov.br/# (last access: 10 January 2025), 2025.
Burnham, K. P. and Anderson, D. R. Model Selection and Multimodel Inference: A Practical Information-Theoretic Approach, 2nd edn., Springer ISBN 978-0387953649, 2002.
Byrne, M. P.: Amplified warming of extreme temperatures over tropical land, Nat. Geosci., 14, 837–841, https://doi.org/10.1038/s41561-021-00828-8, 2021.
Cai, W., McPhaden, M. J., Grimm, A. M., Rodrigues, R. R., Taschetto, A. S., Garreaud, R. D., Dewitte, B., Poveda, G., Ham, Y.-G., Santoso, A., Ng, B., Anderson, W., Wang, G., Geng, T., Jo, H.-S., Marengo, J. A., Alves, L. M., Osman, M., Li, S., Wu, L., Karamperidou, C., Takahashi, K., and Vera, C.: Climate impacts of the El Niño–Southern Oscillation on South America, Nat. Rev. Earth Environ., 1, 215–231, https://doi.org/10.1038/s43017-020-0040-3, 2020.
Cavanaugh, J. E. and Neath, A. A.: The Akaike information criterion: Background, derivation, properties, application, interpretation, and refinements, WIREs Computational Statistics, 11, https://doi.org/10.1002/wics.1460, 2019.
Chen, K., de Schrijver, E., Sivaraj, S., Sera, F., Scovronick, N., Jiang, L., Roye, D., Lavigne, E., Kyselý, J., Urban, A., Schneider, A., Huber, V., Madureira, J., Mistry, M. N., Cvijanovic, I., Gasparrini, A., Vicedo-Cabrera, A. M., Armstrong, B., Schneider, R., Tobias, A., Astrom, C., Guo, Y., Honda, Y., Abrutzky, R., Tong, S., de Sousa Zanotti Stagliorio Coelho, M., Saldiva, P. H. N., Correa, P. M., Ortega, N. V., Kan, H., Osorio, S., Orru, H., Indermitte, E., Jaakkola, J. J. K., Ryti, N., Pascal, M., Katsouyanni, K., Analitis, A., Mayvaneh, F., Entezari, A., Goodman, P., Zeka, A., Michelozzi, P., de'Donato, F., Hashizume, M., Alahmad, B., Diaz, M. H., De la Cruz Valencia, C., Overcenco, A., Houthuijs, D., Ameling, C., Rao, S., Carrasco-Escobar, G., Seposo, X., da Silva, S. P., Holobaca, I. H., Acquaotta, F., Kim, H., Lee, W., Íñiguez, C., Forsberg, B., Ragettli, M. S., Guo, Y. L. L., Pan, S. C., Li, S., Colistro, V., Zanobetti, A., Schwartz, J., Dang, T. N., Van Dung, D., Carlsen, H. K., Cauchi, J. P., Achilleos, S., and Raz, R.: Impact of population aging on future temperature-related mortality at different global warming levels, Nat. Commun., 15, https://doi.org/10.1038/s41467-024-45901-z, 2024.
Cleveland, W. S., Grosse, E., and Shyu, W. M.: Local regression models, in: Statistical Models in S, edited by: Chambers, J. and Hastie, T., Wadsworth & Brooks/Cole ISBN 9780203738535, 1992.
Coles, S.: An Introduction to Statistical Modeling of Extreme Values, Springer London, London, https://doi.org/10.1007/978-1-4471-3675-0, 2001.
Collazo, S.: SoleCollazo/Non-stationary-GEV: NSGEV_rio_v2.1 (v2.1), Zenodo [code], https://doi.org/10.5281/zenodo.13913445, 2024.
Devi, U., Shekhar, M. S., Singh, G. P., Rao, N. N., and Bhatt, U. S.: Methodological application of quantile mapping to generate precipitation data over Northwest Himalaya, Int. J. Climatol., 39, 3160–3170, https://doi.org/10.1002/joc.6008, 2019.
Eastoe, E.: Extreme Value Distributions, Significance, 14, 12–13, https://doi.org/10.1111/j.1740-9713.2017.01014.x, 2017.
Faranda, D. and Alberti, T.: High temperature in Brazil heatwave intensified by both human-driven Climate Change and Natural Variability, ClimaMeter, Institut Pierre Simon Laplace, CNRS, https://www.climameter.org/20240315-18-brazil-heatwave (last access: 4 July 2024), 2024.
Ferreira Correa, L., Folini, D., Chtirkova, B., and Wild, M.: Trends in observed surface solar radiation and their causes in Brazil in the first 2 decades of the 21st century, Atmos. Chem. Phys., 24, 8797–8819, https://doi.org/10.5194/acp-24-8797-2024, 2024.
Ferreira, L. de C. M., Nogueira, M. C., Pereira, R. V. de B., de Farias, W. C. M., Rodrigues, M. M. de S., Teixeira, M. T. B., and Carvalho, M. S.: Ambient temperature and mortality due to acute myocardial infarction in Brazil: an ecological study of time-series analyses, Sci. Rep., 9, https://doi.org/10.1038/s41598-019-50235-8, 2019.
Friederichs, P. and Hense, A.: Statistical Downscaling of Extreme Precipitation Events Using Censored Quantile Regression, Mon. Weather Rev., 135, 2365–2378, https://doi.org/10.1175/MWR3403.1, 2007.
Gasparrini, A.: Distributed Lag Linear and Non-Linear Models in R: The Package dlnm, J. Stat. Softw., 43, https://doi.org/10.18637/jss.v043.i08, 2011.
Gasparrini, A. and Leone, M.: Attributable risk from distributed lag models, BMC Med. Res. Methodol., 14, 55, https://doi.org/10.1186/1471-2288-14-55, 2014.
Gasparrini, A., Armstrong, B., and Kenward, M. G.: Distributed lag non-linear models, Stat. Med., 29, 2224–2234, https://doi.org/10.1002/sim.3940, 2010.
Geirinhas, J. L., Trigo, R. M., Libonati, R., Coelho, C. A. S., and Palmeira, A. C.: Climatic and synoptic characterization of heat waves in Brazil, Int. J. Climatol., 38, 1760–1776, https://doi.org/10.1002/joc.5294, 2018.
Geirinhas, J. L., Russo, A., Libonati, R., Trigo, R. M., Castro, L. C. O., Peres, L. F., Magalhães, M. de A. F. M., and Nunes, B.: Heat-related mortality at the beginning of the twenty-first century in Rio de Janeiro, Brazil, Int. J. Biometeorol., 64, 1319–1332, https://doi.org/10.1007/s00484-020-01908-x, 2020.
Geirinhas, J. L., Russo, A., Libonati, R., Sousa, P. M., Miralles, D. G., and Trigo, R. M.: Recent increasing frequency of compound summer drought and heatwaves in Southeast Brazil, Environ. Res. Lett., 16, https://doi.org/10.1088/1748-9326/abe0eb, 2021.
Godoy, M. L. D. P., Godoy, J. M., Roldão, L. A., Soluri, D. S., and Donagemma, R. A.: Coarse and fine aerosol source apportionment in Rio de Janeiro, Brazil, Atmos. Environ., 43, 2366–2374, https://doi.org/10.1016/j.atmosenv.2008.12.046, 2009.
Grillakis, M. G., Polykretis, C., Manoudakis, S., Seiradakis, K. D., and Alexakis, D. D.: A Quantile Mapping Method to Fill in Discontinued Daily Precipitation Time Series, Water (Basel), 12, 2304, https://doi.org/10.3390/w12082304, 2020.
Grimm, A. M.: The El Niño Impact on the Summer Monsoon in Brazil: Regional Processes versus Remote Influences, J. Climate, 16, 263–280, https://doi.org/10.1175/1520-0442(2003)016<0263:TENIOT>2.0.CO;2, 2003.
Heffernan, J. E. and Stephenson, A. G.: ismev: An introduction to statistical modeling of extreme values (Version 1.42), R package https://CRAN.R-project.org/package=ismev (last access: 13 May 2025), 2018.
Hollister, J., Shah, T., Nowosad, J., Robitaille, A., Beck, M., and Johnson, M.: elevatr: Access Elevation Data from Various APIs (R package version 0.99.0), Zenodo [code], https://doi.org/10.5281/zenodo.8335450, 2023.
Huang, W. K., Stein, M. L., McInerney, D. J., Sun, S., and Moyer, E. J.: Estimating changes in temperature extremes from millennial-scale climate simulations using generalized extreme value (GEV) distributions, Adv. Stat. Climatol. Meteorol. Oceanogr., 2, 79–103, https://doi.org/10.5194/ascmo-2-79-2016, 2016.
Infante Gil, S. and Zarate de Lara, G.: Metodos Estadisticos: un enfoque interdisciplinario, Trillas, Mexico, 643 pp., ISBN 9789682414220, 1984.
IPCC: Summary for Policymakers. In: Climate Change 2023: Synthesis Report. Contribution of Working Groups I, II and III to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change, edited by: Core Writing Team, Lee, H., and Romero, J., IPCC, Geneva, Switzerland, 1–34, https://doi.org/10.59327/IPCC/AR6-9789291691647.001, 2023.
Jézéquel, A., Dépoues, V., Guillemot, H., Trolliet, M., Vanderlinden, J.-P., and Yiou, P.: Behind the veil of extreme event attribution, Climatic Change, 149, 367–383, https://doi.org/10.1007/s10584-018-2252-9, 2018.
Joshi, M. M., Gregory, J. M., Webb, M. J., Sexton, D. M. H., and Johns, T. C.: Mechanisms for the land/sea warming contrast exhibited by simulations of climate change, Clim. Dynam., 30, 455–465, https://doi.org/10.1007/s00382-007-0306-1, 2008.
Katz, R.W.: Statistical Methods for Nonstationary Extremes, edited by: AghaKouchak, A., Easterling, D., Hsu, K., Schubert, S., and Sorooshian, S., Extremes in a Changing Climate, Water Science and Technology Library, Springer, Dordrecht, 65, https://doi.org/10.1007/978-94-007-4479-0_2, 2023
Kendall, M. G.: Rank Correlation Methods, 4th edition., Charles Griffin, London ISBN 9780852641996, 1975.
Kew, S., Pinto, I., Alves, L., Santos, D., Libonati, R., Philip, S., Zachariah, M., Barnes, C., Kimutai, J., Vahlberg, M., Arrighi, J., Otto, F., and Clark, B.: Strong influence of climate change in uncharacteristic early spring heat in South America Main findings, World Weather Attribution, https://spiral.imperial.ac.uk/server/api/core/bitstreams/df7bf07a-2b33-4ad4-8dd7-23856cdc8f27/content (last access: 4 September 2025), 2023.
Kharin, V. V. and Zwiers, F. W.: Estimating Extremes in Transient Climate Change Simulations, J. Climate, 18, 1156–1173, https://doi.org/10.1175/JCLI3320.1, 2005.
Krüger, E., Gobo, J. P. A., Tejas, G. T., da Silva de Souza, R. M., Neto, J. B. F., Pereira, G., Mendes, D., and Di Napoli, C.: The impact of urbanization on heat stress in Brazil: A multi-city study, Urban Clim., 53, https://doi.org/10.1016/j.uclim.2024.101827, 2024.
Liu, Z., Zhan, W., Bechtel, B., Voogt, J., Lai, J., Chakraborty, T., Wang, Z.-H., Li, M., Huang, F., and Lee, X.: Surface warming in global cities is substantially more rapid than in rural background areas, Commun. Earth Environ., 3, 219, https://doi.org/10.1038/s43247-022-00539-x, 2022.
Luiz-Silva, W. and Garcia, K. C.: Sustainable future and water resources: a synthesis of the Brazilian hydroelectricity sector in face of climate change, Sustain. Water Resour. Manag., 8, 120, https://doi.org/10.1007/s40899-022-00711-3, 2022.
Luiz-Silva, W. and Oscar-Júnior, A. C.: Climate extremes related with rainfall in the State of Rio de Janeiro, Brazil: a review of climatological characteristics and recorded trends, Nat. Hazards, 114, 713–732, https://doi.org/10.1007/s11069-022-05409-5, 2022.
Lüthi, S., Fairless, C., Fischer, E. M., Scovronick, N., Ben Armstrong, Coelho, M. D. S. Z. S., Guo, Y. L., Guo, Y., Honda, Y., Huber, V., Kyselý, J., Lavigne, E., Royé, D., Ryti, N., Silva, S., Urban, A., Gasparrini, A., Bresch, D. N., and Vicedo-Cabrera, A. M.: Rapid increase in the risk of heat-related mortality, Nat. Commun., 14, https://doi.org/10.1038/s41467-023-40599-x, 2023.
Mann, H. B.: Nonparametric Tests Against Trend, Econometrica, 13, 245, https://doi.org/10.2307/1907187, 1945.
McGregor, S., Cassou, C., Kosaka, Y., and Phillips, A. S.: Projected ENSO Teleconnection Changes in CMIP6, Geophys. Res. Lett., 49, https://doi.org/10.1029/2021GL097511, 2022.
MEASURE Evaluation: Barriers to use of health data in low- and middle-income countries: A review of the literature, Working Paper No. WP-18-211, University of North Carolina at Chapel Hill, https://www.measureevaluation.org/resources/publications/wp-18-211/at_download/document (last access: 4 September 2025), 2018.
Met Office: Global mean temperature anomalies, Met Office [data set], https://www.metoffice.gov.uk/hadobs/hadcrut5/, last access: 29 July 2024.
Mohammadi, T., Moghaddasi, M., Anvari, S., and Aziz, R.: Estimation of non-stationary return levels of extreme temperature by CMIP6 models, Water Pract. Technol., 19, 594–610, https://doi.org/10.2166/wpt.2024.010, 2024.
NAS: Attribution of Extreme Weather Events in the Context of Climate Change, National Academies Press, Washington, D.C., https://doi.org/10.17226/21852, 2016.
NOAA: ENSO, NOAA [data set], https://psl.noaa.gov/data/correlation/nina34.anom.data, last access: 13 May 2025.
van Oldenborgh, G. J., Philip, S., Kew, S., van Weele, M., Uhe, P., Otto, F., Singh, R., Pai, I., Cullen, H., and AchutaRao, K.: Extreme heat in India and anthropogenic climate change, Nat. Hazards Earth Syst. Sci., 18, 365–381, https://doi.org/10.5194/nhess-18-365-2018, 2018.
Van Oldenborgh, G. J., Wehner, M. F., Vautard, R., Otto, F. E. L., Seneviratne, S. I., Stott, P. A., Hegerl, G. C., Philip, S. Y., and Kew, S. F.: Attributing and Projecting Heatwaves Is Hard: We Can Do Better, https://doi.org/10.1029/2021EF002271, 2022.
de Oliveira, M. M. F., de Oliveira, J. L. F., Fernandes, P. J. F., Gilleland, E., and Ebecken, N. F. F.: Extreme climatic characteristics near the coastline of the southeast region of Brazil in the last 40 years, Theor. Appl. Climatol., 146, 657–674, https://doi.org/10.1007/s00704-021-03711-z, 2021.
de Oliveira-Júnior, J. F., de Gois, G., de Bodas Terassi, P. M., da Silva Junior, C. A., Blanco, C. J. C., Sobral, B. S., and Gasparini, K. A. C.: Drought severity based on the SPI index and its relation to the ENSO and PDO climatic variability modes in the regions North and Northwest of the State of Rio de Janeiro – Brazil, Atmos. Res., 212, 91–105, https://doi.org/10.1016/j.atmosres.2018.04.022, 2018.
Olonscheck, D., Schurer, A. P., Lücke, L., and Hegerl, G. C.: Large-scale emergence of regional changes in year-to-year temperature variability by the end of the 21st century, Nat. Commun., 12, 7237, https://doi.org/10.1038/s41467-021-27515-x, 2021.
Otto, F. E. L.: Attribution of Weather and Climate Events, Annu. Rev. Environ. Resour., 42, 627–646, https://doi.org/10.1146/annurev-environ-102016-060847, 2017.
Ouarda, T. B. M. J., Charron, C., and St-Hilaire, A.: Uncertainty of stationary and nonstationary models for rainfall frequency analysis, Int. J. Climatol., 40, 2373–2392, https://doi.org/10.1002/joc.6339, 2020.
Pereira, L. B., Martins, L. L., Rodrigues, I. C. A., Sobierajski, G. R., and Blain, G. C. Changes in Extreme Air Temperature in One of South America's Longest Meteorological Records: Campinas, Brazil (1890–2022), Bragantia, 82, e20230128, https://doi.org/10.1590/1678-4499.20230128, 2023
Peres, L. de F., Lucena, A. J., Rotunno Filho, O. C., and França, J. R. A.: The urban heat island in Rio de Janeiro, Brazil, in the last 30 years using remote sensing data, Int. J. Appl. Earth Obs., 64, 104–116, https://doi.org/10.1016/j.jag.2017.08.012, 2018.
Perkins-Kirkpatrick, S., Barriopedro, D., Jha, R., Wang, L., Mondal, A., Libonati, R., and Kornhuber, K.: Extreme terrestrial heat in 2023, https://doi.org/10.1038/s43017-024-00536-y, 2024.
Philip, S., Kew, S., van Oldenborgh, G. J., Otto, F., Vautard, R., van der Wiel, K., King, A., Lott, F., Arrighi, J., Singh, R., and van Aalst, M.: A protocol for probabilistic extreme event attribution analyses, Adv. Stat. Clim. Meteorol. Oceanogr., 6, 177–203, https://doi.org/10.5194/ascmo-6-177-2020, 2020.
Prosdocimi, D. and Klima, K.: Health effects of heat vulnerability in Rio de Janeiro: a validation model for policy applications, SN Appl. Sci., 2, 1948, https://doi.org/10.1007/s42452-020-03750-7, 2020.
Reboita, M. S., Ambrizzi, T., Crespo, N. M., Dutra, L. M. M., Ferreira, G. W. de S., Rehbein, A., Drumond, A., da Rocha, R. P., and Souza, C. A. de: Impacts of teleconnection patterns on South America climate, Ann. NY Acad. Sci., 1504, 116–153, https://doi.org/10.1111/nyas.14592, 2021.
Regoto, P., Dereczynski, C., Chou, S. C., and Bazzanela, A. C.: Observed changes in air temperature and precipitation extremes over Brazil, Int. J. Climatol., 41, 5125–5142, https://doi.org/10.1002/joc.7119, 2021.
Rehfeld, K., Hébert, R., Lora, J. M., Lofverstrom, M., and Brierley, C. M.: Variability of surface climate in simulations of past and future, Earth Syst. Dynam., 11, 447–468, https://doi.org/10.5194/esd-11-447-2020, 2020.
Robin, Y. and Ribes, A.: Nonstationary extreme value analysis for event attribution combining climate models and observations, Adv. Stat. Clim. Meteorol. Oceanogr., 6, 205–221, https://doi.org/10.5194/ascmo-6-205-2020, 2020.
Schwarz, G.: Estimating the Dimension of a Model, Ann. Stat., 6, https://doi.org/10.1214/aos/1176344136, 1978.
Secretaria de Estado de Saude of Rio de Janeiro: Mortality in the State of Rio de Janeiro, Secretaria de Estado de Saude of Rio de Janeiro [data set], http://sistemas.saude.rj.gov.br/tabnetbd/dhx.exe?sim/tf_sim_do_geral.def, last access: 23 December 2024.
Sen, P. K.: Estimates of the Regression Coefficient Based on Kendall's Tau, J. Am. Stat. Assoc., 63, 1379, https://doi.org/10.2307/2285891, 1968.
Seneviratne, S. I., Corti, T., Davin, E. L., Hirschi, M., Jaeger, E. B., Lehner, I., Orlowsky, B., and Teuling, A. J.: Investigating soil moisture-climate interactions in a changing climate: A review, Earth Sci. Rev., 99, 125–161, https://doi.org/10.1016/j.earscirev.2010.02.004, 2010.
Shepherd, T. G.: A Common Framework for Approaches to Extreme Event Attribution, https://doi.org/10.1007/s40641-016-0033-y, 2016.
Sheridan, S. C., Lee, C. C., and Smith, E. T.: A Comparison Between Station Observations and Reanalysis Data in the Identification of Extreme Temperature Events, Geophys. Res. Lett., 47, https://doi.org/10.1029/2020GL088120, 2020.
Silva, W. L. and Dereczynski, C. P.: Caracterização Climatológica e Tendências observadas em extremos climáticos no Estado do Rio de Janeiro, Anuario do Instituto de Geociencias, 37, 123–138, https://doi.org/10.11137/2014_2_123_138, 2014.
Silveira, I. H., Cortes, T. R., Bell, M. L., and Junger, W. L.: Effects of heat waves on cardiovascular and respiratory mortality in Rio de Janeiro, Brazil, PLoS One, 18, https://doi.org/10.1371/journal.pone.0283899, 2023.
Slater, L. J., Anderson, B., Buechel, M., Dadson, S., Han, S., Harrigan, S., Kelder, T., Kowal, K., Lees, T., Matthews, T., Murphy, C., and Wilby, R. L.: Nonstationary weather and water extremes: a review of methods for their detection, attribution, and management, Hydrol. Earth Syst. Sci., 25, 3897–3935, https://doi.org/10.5194/hess-25-3897-2021, 2021.
Smirnov, N.: Table for Estimating the Goodness of Fit of Empirical Distributions, Ann. Math. Stat., 19, 279–281, 1948.
Sobral, B. S., de Oliveira-Júnior, J. F., de Gois, G., Pereira-Júnior, E. R., Terassi, P. M. de B., Muniz-Júnior, J. G. R., Lyra, G. B., and Zeri, M.: Drought characterization for the state of Rio de Janeiro based on the annual SPI index: trends, statistical tests and its relation with ENSO, Atmos. Res., 220, 141–154, https://doi.org/10.1016/j.atmosres.2019.01.003, 2019.
Solórzano, A., Brasil-Machado, A., and Ribeiro de Oliveira, R.: Land use and social-ecological legacies of Rio de Janeiro's Atlantic urban forests: from charcoal production to novel ecosystems, R. Soc. Open Sci., 8, 201855, https://doi.org/10.1098/rsos.201855, 2021.
Stott, P. A., Christidis, N., Otto, F. E. L., Sun, Y., Vanderlinden, J., van Oldenborgh, G. J., Vautard, R., von Storch, H., Walton, P., Yiou, P., and Zwiers, F. W.: Attribution of extreme weather and climate-related events, WIREs Clim. Change, 7, 23–41, https://doi.org/10.1002/wcc.380, 2016.
Sutton, R. T., Dong, B., and Gregory, J. M.: Land/sea warming ratio in response to climate change: IPCC AR4 model results and comparison with observations, Geophys. Res. Lett., 34, https://doi.org/10.1029/2006GL028164, 2007.
Tobías, A., Armstrong, B., and Gasparrini, A.: Brief Report, Epidemiology, 28, 72–76, https://doi.org/10.1097/EDE.0000000000000567, 2017.
Tobías, A., Íñiguez, C., and Royé, D.: From Research to the Development of an Innovative Application for Monitoring Heat-Related Mortality in Spain, Environ. Health, 1, 416–419, https://doi.org/10.1021/envhealth.3c00134, 2023.
Tomasella, J., Cunha, A. P. M. A., Simões, P. A., and Zeri, M.: Assessment of trends, variability and impacts of droughts across Brazil over the period 1980–2019, Nat. Hazards, https://doi.org/10.1007/s11069-022-05759-0, 2022.
United Nations Framework Convention on Climate Change: Paris Agreement, UNFCCC, Report No. FCCC/CP/2015/L.9/Rev.1, https://unfccc.int/sites/default/files/english_paris_agreement.pdf (last access: 4 September 2025), 2015.
Vautard, R., van Aalst, M., Boucher, O., Drouin, A., Haustein, K., Kreienkamp, F., van Oldenborgh, G. J., Otto, F. E. L., Ribes, A., Robin, Y., Schneider, M., Soubeyroux, J.-M., Stott, P., Seneviratne, S. I., Vogel, M. M., and Wehner, M.: Human contribution to the record-breaking June and July 2019 heatwaves in Western Europe, Environ. Res. Lett., 15, 094077, https://doi.org/10.1088/1748-9326/aba3d4, 2020.
Wallace, C. J. and Joshi, M.: Comparison of land–ocean warming ratios in updated observed records and CMIP5 climate models, Environ. Res. Lett., 13, 114011, https://doi.org/10.1088/1748-9326/aae46f, 2018.
Wanderley, H. S., Fernandes, R. C., and De Carvalho, A. L.: Aumento das temperaturas extremas na cidade do Rio de Janeiro e o desvio ocasionado durante um evento de El Niño intenso (Thermal change in the city of Rio de Janeiro and the deviation caused during an intense El Niño event), Revista Brasileira de Geografia Física, 12, 1291–1301, https://doi.org/10.26848/rbgf.v12.4.p1291-1301, 2019.
Wang, Q., Fan, X., and Wang, M.: Evidence of high-elevation amplification versus Arctic amplification, Sci. Rep., 6, 19219, https://doi.org/10.1038/srep19219, 2016.
Wehner, M., Stone, D., Shiogama, H., Wolski, P., Ciavarella, A., Christidis, N., and Krishnan, H.: Early 21st century anthropogenic changes in extremely hot days as simulated by the C20C+ detection and attribution multi-model ensemble, Weather Clim. Extrem., 20, 1–8, https://doi.org/10.1016/j.wace.2018.03.001, 2018.
Zhao, Q., Li, S., Coelho, M. de S. Z. S., Saldiva, P. H. N., Xu, R., Huxley, R. R., Abramson, M. J., and Guo, Y.: Ambient heat and hospitalisation for COPD in Brazil: a nationwide case-crossover study, Thorax, 74, 1031–1036, https://doi.org/10.1136/thoraxjnl-2019-213486, 2019a.
Zhao, Q., Li, S., Coelho, M. S. Z. S., Saldiva, P. H. N., Hu, K., Arblaster, J. M., Nicholls, N., Huxley, R. R., Abramson, M. J., and Guo, Y.: Geographic, demographic, and temporal variations in the association between heat exposure and hospitalization in Brazil: A nationwide study between 2000 and 2015, Environ. Health Persp., 127, https://doi.org/10.1289/EHP3889, 2019b.
Short summary
In 2023, Rio de Janeiro experienced record-breaking heat waves linked to climate change and El Niño. Our study shows that global warming made these extreme temperatures at least 2 °C hotter than in pre-industrial times. Heat-related deaths surged, with climate change contributing to one in three fatalities during the peak event. Without adaptation, future heat waves will claim even more lives. This underscores the urgent need for policies to mitigate climate impacts from escalating heat threats.
In 2023, Rio de Janeiro experienced record-breaking heat waves linked to climate change and El...
Altmetrics
Final-revised paper
Preprint