# Global Climate [in "State of the Climate in 2024"]

R. Dunn, J. Blannin, K. Willett, N. Gobron, G. Morris, Melanie Ades, Robert Adler, Mihai Alexe, Richard Allan, John Anderson, et al.

<figure><img src="image_1.png" /></figure>

## To cite this version:

R. Dunn, J. Blannin, K. Willett, N. Gobron, G. Morris, et al., Global Climate [in "State of the Climate in 2024"]. Bulletin of the American Meteorological Society, 2025, 106 (8), pp.S11-S172. 10.1175/BAMS-D-25-0102.1. insu-05525230

HAL Id: insu-05525230

https://insu.hal.science/insu-05525230v1

Submitted on 25 Feb 2026

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Distributed under a Creative Commons CC BY 4.0 - Attribution - International License
# STATE OF THE CLIMATE IN 2024

## GLOBAL CLIMATE

R. J. H. Dunn, J. Blannin, K. M. Willett, N. Gobron, and G. A. Morris, Eds.

<figure><img src="image_3.png" /></figure>

Special Online Supplement to the *Bulletin of the American Meteorological Society* Vol. 106, No. 8, August, 2025

https://doi.org/10.1175/BAMS-D-25-0102.1

Corresponding author: Robert Dunn / robert.dunn@metoffice.gov.uk

©2025 American Meteorological Society

For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy.
# STATE OF THE CLIMATE IN 2024
## Global Climate

**Editors**

Jessica Blunden
James Reagan

**Chapter Editors**

Anthony Arguez
Josh Blannin
Peter Bissolli
Kyle R. Clem
Howard J. Diamond
Matthew L. Druckenmiller
Robert J. H. Dunn
Catherine Ganter
Nadine Gobron
Gregory C. Johnson
Rick Lumpkin
Rodney Martinez
Ademe Mekonnen
Twila A. Moon
Gary A. Morris
Marilyn N. Raphael
Carl J. Schreck III
Laura Stevens
Richard L. Thoman
Kate M. Willett
Zhiwei Zhu

**Technical Editor**

Lukas Noguchi

**BAMS Special Editor for Climate**

Timothy DelSole

**American Meteorological Society**
**Cover Credit:**

Land Surface Temperature (LST) view of London, UK, on 30 July 2024 from Sentinel 3a. The LST data have been downscaled using Vis/SWIR inputs from Landsat 9 and Sentinel 2a and a bottom-of-atmosphere optimal estimation methodology to refine the estimates from 1 km data to 100 m.

Credit: M. Perry, C. Paton, B. Courtier, D. Ghent, NCEO, University of Leicester

**How to cite this document:**

Global Climate is one chapter from the *State of the Climate in 2024* annual report and is available from https://doi.org/10.1175/BAMS-D-25-0102.1. Compiled by NOAA's National Centers for Environmental Information, *State of the Climate in 2024* is based on contributions from scientists from around the world. It provides a detailed update on global climate indicators, notable weather events, and other data collected by environmental monitoring stations and instruments located on land, water, ice, and in space. The full report is available from https://doi.org/10.1175/2025BAMSStateoftheClimate.1.

**Citing the complete report:**

Blunden, J. and J. Reagan, Eds., 2025: "State of the Climate in 2024". Bull. Amer. Meteor. Soc., 106 (8), Si-S513 https://doi.org/10.1175/2025BAMSStateoftheClimate.1.

**Citing this chapter:**

Dunn, R. J. H., J. Blannin, K. M. Willett, N. Gobron, and G. A. Morris, Eds., 2025: Global Climate [in "State of the Climate in 2024"). Bull. Amer. Meteor. Soc., 106 (8), S11-S172, https://doi.org/10.1175/BAMS-D-25-0102.1.

**Citing a section (example):**

Fereday, D., D. Campos, and G. Macara, 2025: Mean sea level pressure and related modes of variability [in "State of the Climate in 2024"). Bull. Amer. Meteor. Soc., 106 (8), S80-S81, https://doi.org/10.1175/BAMS-D-25-0102.1.
Editor and Author Affiliations (alphabetical by name)

**Ades, Melanie**, European Centre for Medium-Range Weather Forecasts, Reading, United Kingdom

**Adler, Robert**, Earth System Science Interdisciplinary Center, University of Maryland, College Park, Maryland

**Alexe, Mihai**, European Centre for Medium-Range Weather Forecasts, Bonn, Germany

**Allan, Richard P.**, Department of Meteorology, University of Reading, Reading, United Kingdom; National Centre for Earth Observation (NCEO), University of Leicester, Leicester, United Kingdom

**Anderson, John**, Hampton University, Hampton, Virginia

**Anneville, Orlane**, National Research Institute for Agriculture, Food and Environment (INRAE), CARRTEL, Université Savoie Mont Blanc, Thonon les Bains, France

**Aono, Yasuyuki**, Graduate School of Agriculture, Osaka Metropolitan University, Sakai, Japan

**Arguez, Anthony**, NOAA/NESDIS National Centers for Environmental Information, Asheville, North Carolina

**Armenteras-Pascual, Dolors**, Science Faculty, Universidad Nacional de Colombia, Bogotá, Colombia

**Arosio, Carlo**, University of Bremen, Bremen, Germany

**Asher, Elizabeth**, Cooperative Institute for Research in Environmental Sciences, University of Colorado Boulder, Boulder, Colorado; NOAA Global Monitoring Laboratory, Boulder, Colorado

**Augustine, John A.**, NOAA Global Monitoring Laboratory, Boulder, Colorado

**Azorin-Molina, Cesar**, Centro de Investigaciones sobre Desertificación – Spanish National Research Council (CSIC-UV-GVA), Valencia, Spain

**Baez-Villanueva, Oscar M.**, Hydro-Climate Extremes Lab (H-CEL), Ghent University, Ghent, Belgium

**Barichivich, Jonathan**, Laboratoire des Sciences du Climat et de l'Environnement (LSCE), LSCE/IPSL, CEA-CNRS-UVSQ, Gif-sur-Yvette, France

**Baron, Alexandre**, Cooperative Institute for Research in Environmental Sciences, University of Colorado Boulder, Boulder, Colorado; NOAA Chemical Sciences Laboratory, Boulder, Colorado

**Beck, Hylke E.**, Physical Science and Engineering Division, King Abdullah University of Science and Technology, Thuwal, Saudi Arabia

**Bellouin, Nicolas**, University of Reading, Reading, United Kingdom; Institute Pierre Simon Laplace (IPSL), Guyancourt, France

**Benedetti, Angela**, European Centre for Medium-Range Weather Forecasts, Reading, United Kingdom

**Blannin, Josh**, Met Office Hadley Centre, Exeter, United Kingdom

**Blenkinsop, Stephen**, School of Engineering, Newcastle University, Newcastle-upon-Tyne, United Kingdom; Tyndall Centre for Climate Change Research, Norwich, United Kingdom

**Bock, Olivier**, Université Paris Cité, Institut de physique du globe de Paris, CNRS, IGN, Paris, France; Univ Gustave Eiffel, ENSG, IGN, Marne-la-Vallée, France

**Bodin, Xavier**, Laboratoire EDYTEM, CNRS/Université Savoie Mont-Blanc, Le Bourget-du-Lac, France

**Bonte, Olivier**, Hydro-Climate Extremes Lab (H-CEL), Ghent University, Ghent, Belgium

**Bosilovich, Michael G.**, Global Modeling and Assimilation Office, NASA Goddard Space Flight Center, Greenbelt, Maryland

**Boucher, Olivier**, Institute Pierre Simon Laplace (IPSL), Guyancourt, France

**Bowman, Kevin**, Jet Propulsion Laboratory, California Institute of Technology, Pasadena, California

**Buehler, Sarah A.**, Universität Hamburg, Hamburg, Germany

**Bunno, Ayaka**, Japan Meteorological Agency, Tokyo, Japan

**Byrne, Michael**, P., School of Earth and Environmental Sciences, University of St Andrews, St Andrews, United Kingdom

**Campos, Diego**, Barcelona Supercomputing Centre, Barcelona, Spain

**Cappucci, Fabrizio**, Joint Research Centre, European Commission, Ispra, Italy

**Carrea, Laura**, University of Reading, Reading, United Kingdom

**Casado-Rodríguez, Jesús**, Joint Research Centre, European Commission, Ispra, Italy

**Chang, Kai-Lan**, Cooperative Institute for Research in Environmental Sciences, University of Colorado Boulder, Boulder, Colorado; NOAA Chemical Sciences Laboratory, Boulder, Colorado

**Christiansen, Hanne H.**, Arctic Geophysics Department, University Centre in Svalbard, Longyearbyen, Norway

**Christy, John R.**, University of Alabama in Huntsville, Huntsville, Alabama

**Chung, Eui-Seok**, Korea Polar Research Institute, Incheon, South Korea

**Ciasto, Laura M.**, NOAA/NWS National Centers for Environmental Prediction, Climate Prediction Center, College Park, Maryland

**Clingan, Scott**, Cooperative Institute for Research in Environmental Sciences, University of Colorado Boulder, Boulder, Colorado; NOAA Global Monitoring Laboratory, Boulder, Colorado

**Coldewey-Egbers, Melanie**, German Aerospace Center (DLR), Oberpfaffenhofen, Germany

**Cooley, Sarah**, Duke University, Durham, North Carolina

**Cooper, Owen R.**, NOAA Chemical Sciences Laboratory, Boulder, Colorado

**Cornes, Richard C.**, National Oceanography Centre, Southampton, United Kingdom

**Covey, Curt**, Lawrence Livermore National Laboratory, Livermore, California

**Crétaux, Jean-François**, LEGOS (CNES/CNRS/IRD/UPS), Université de Toulouse, Toulouse, France

**Crimmins, Theresa**, USA National Phenology Network, School of Natural Resources and the Environment, University of Arizona, Tucson, Arizona

**Crotwell, Molly**, Cooperative Institute for Research in Earth Science, University of Colorado Boulder, Boulder, Colorado; NOAA Global Monitoring Laboratory, Boulder, Colorado

**Culpepper, Joshua**, Department of Biology, York University, Toronto, Canada

**Cusicanqui, Diego**, Institut de Géosciences de l'Environnement (IGE), Université Grenoble Alpes, INRAE, CNRS, IRD, Grenoble INP, ITerre, Grenoble, France

**Davis, Sean M.**, NOAA Chemical Sciences Laboratory, Boulder, Colorado

**de Jeu, Richard A. M.**, Transmissivity B.V., Haarlem, The Netherlands

**de Laat, Jos**, Royal Netherlands Meteorological Institute (KNMI), DeBilt, The Netherlands

**Degenstein, Doug**, University of Saskatchewan, Saskatoon, Canada

**Delaloye, Reynald**, Department of Geosciences, University of Fribourg, Fribourg, Switzerland

**Di Tomaso, Enza**, European Centre for Medium-Range Weather Forecasts, Bonn, Germany

**Dokulil, Martin T.**, Research Institute for Limnology, University of Innsbruck, Mondsee, Austria

**Donat, Markus G.**, Barcelona Supercomputing Centre, Barcelona, Spain; Catalan Institution for Research and Advanced Studies (ICREA), Barcelona, Spain

**Dorigo, Wouter A.**, Department of Geodesy and Geoinformation, TU Wien, Vienna, Austria

**Dugan, Hilary**, Center of Limnology, University of Wisconsin-Madison, Madison, Wisconsin

**Dunn, Robert J. H.**, Met Office Hadley Centre, Exeter, United Kingdom

**Durre, Imke**, Climate Science and Services Division, NOAA/NESDIS National Centers for Environmental Information, Asheville, North Carolina

**Dutton, Geoff**, Cooperative Institute for Research in Earth Science, University of Colorado Boulder, Boulder, Colorado

**Effertz, Peter**, Cooperative Institute for Research in Environmental Sciences, University of Colorado Boulder, Boulder, Colorado; NOAA Global Monitoring Laboratory, Boulder, Colorado

**Enno, Sven-Erik**, EUMETSAT, Darmstadt, Germany

**Estilow, Thomas W.**, Rutgers University, Piscataway, New Jersey

**Estrella, Nicole**, Ecoclimatology, Department of Life Science Systems, TUM School of Life Sciences, Technical University of Munich, Freising, Germany

**Fereday, David**, Met Office Hadley Centre, Exeter, United Kingdom
Editor and Author Affiliations (continued)

Fioletov, Vitali E., Environment and Climate Change Canada, Toronto, Canada

Flemming, Johannes, European Centre for Medium-Range Weather Forecasts,
Bonn, Germany

Formanek, Maud, Department of Geodesy and Geoinformation, TU Wien, Vienna,
Austria

Foster, Michael J., Cooperative Institute for Meteorological Satellite Studies, Space
Science and Engineering Center, University of Wisconsin-Madison, Madison,
Wisconsin

Frederikse, Thomas, Planet Labs, Haarlem, The Netherlands

Frith, Stacey M., Science Systems and Applications, Inc, Lanham, Maryland; NASA
Goddard Space Flight Center, Greenbelt, Maryland

Froidevaux, Lucien, Jet Propulsion Laboratory, California Institute of Technology,
Pasadena, California

Füllekrug, Martin, University of Bath, Bath, United Kingdom

Gallemann, Thomas, Bavarian Environment Agency, Augsburg, Germany

Garforth, Judith, Woodland Trust, Grantham, United Kingdom

Garg, Jay, ADNET Systems, Inc., Hampton, Virginia

Ghent, Darren, National Centre for Earth Observation (NCEO), University of
Leicester, Leicester, United Kingdom; School of Physics and Astronomy,
University of Leicester, Leicester, United Kingdom

Gobron, Nadine, Joint Research Centre, European Commission, Ispra, Italy

Gollop, Amee, Climatic Research Unit, School of Environmental Sciences, University
of East Anglia, Norwich, United Kingdom

Good, Elizabeth, Met Office, Exeter, United Kingdom

Goodman, Steven, Thunderbolt Global Analytics, Huntsville, Alabama

Goto, Atsushi, Japan Meteorological Agency, Tokyo, Japan

Grimaldi, Stefania, Joint Research Centre, European Commission, Ispra, Italy

Gruber, Alexander, Department of Geodesy and Geoinformation, TU Wien, Vienna,
Austria

Gu, Guojun, Earth System Science Interdisciplinary Center, University of Maryland,
College Park, Maryland

Guglielmin, Mauro, Department of Theoretical and Applied Sciences, Insubria
University, Varese, Italy

Hagho doost, Shekoofeh, Hydro-Climate Extremes Lab (H-CEL), Ghent University,
Ghent, Belgium

Hahn, Sebastian, Department of Geodesy and Geoinformation, TU Wien, Vienna,
Austria

Haimberger, Leopold, Department of Meteorology and Geophysics, University of
Vienna, Austria

Hall, Brad D., NOAA Global Monitoring Laboratory, Boulder, Colorado

Harlan, Merritt E., U.S. Geological Survey, Denver, Colorado

Harris, Bethan L., National Centre for Earth Observation (NCEO), University of
Leicester, Leicester, United Kingdom; United Kingdom Centre for Ecology &
Hydrology, Wallingford, United Kingdom

Harris, Ian, National Centre for Atmospheric Science (NCAS), University of
East Anglia, Norwich, United Kingdom; Climatic Research Unit, School of
Environmental Sciences, University of East Anglia, Norwich, United Kingdom

Hemming, Deborah L., Met Office Hadley Centre, Exeter, United Kingdom;
Birmingham Institute of Forest Research, Birmingham University, Birmingham,
United Kingdom

Ho, Shu-peng (Ben), NOAA/NESDIS Center for Satellite Applications and Research,
College Park, Maryland

Holliday, Rebecca, Met Office, Exeter, United Kingdom

Holzworth, Robert, University of Washington, Seattle, Washington

Horton, Radley M., Columbia Climate School, Columbia University, New York, New
York

Hrbáček, Filip, Department of Geography, Masaryk University, Brno, Czech Republic

Hu, Guojie, Cryosphere Research Station on Qinghai-Tibet Plateau, Northwestern
Institute of Eco-Environment and Resources, CAS, Lanzhou, China

Inness, Antje, European Centre for Medium-Range Weather Forecasts, Reading,
United Kingdom

Isaksen, Ketil, Norwegian Meteorological Institute, Oslo, Norway

John, Viju O., EUMETSAT, Darmstadt, Germany

Jones, Philip D., Climatic Research Unit, School of Environmental Sciences,
University of East Anglia, Norwich, United Kingdom

Junod, Robert, Earth System Science Center, University of Alabama in Huntsville,
Huntsville, Alabama

Kääb, Andreas, Department of Geosciences, University of Oslo, Norway

Kaiser, Johannes W., Norwegian Institute for Air Research (NILU), Kjeller, Norway

Kaufmann, Viktor, Institute of Geodesy, Working Group Remote Sensing and
Photogrammetry, Graz University of Technology, Graz, Austria

Kellerer-Pirklbauer, Andreas, Department of Geography and Regional Science,
Cascade—The Mountain Processes and Mountain Hazards Group, University of
Graz, Graz, Austria

Kent, Elizabeth C., National Oceanography Centre, Southampton, United Kingdom

Khaykin, Sergey, Laboratoire Atmosphères, Observations Spatiales (LATMOS),
UVSQ, Sorbonne Université, CNRS, IPSL, Guyancourt, France

Kidd, Richard, EODC GmbH, Vienna, Austria

Kipling, Zak, European Centre for Medium-Range Weather Forecasts, Reading,
United Kingdom

Kirkpatrick, Sarah, Fenner School of Environment and Society, Australian National
University, Canberra, Australia

Kondragunta, Shobha, NOAA/NESDIS, College Park, Maryland

Kovács, Dávid D., Department of Geodesy and Geoinformation, TU Wien, Vienna,
Austria

Kraemer, Benjamin M., University of Freiburg, Freiburg, Germany

Laas, Alo, Estonian University of Life Sciences, Tartumaa, Estonia

Lan, Xin, Cooperative Institute for Research in the Environmental Sciences,
University of Colorado Boulder, Boulder, Colorado; NOAA Global Monitoring
Laboratory, Boulder, Colorado

Lantz, Kathleen O., NOAA Global Monitoring Laboratory, Boulder, Colorado

Lavers, David A., European Centre for Medium-Range Weather Forecasts, Reading,
United Kingdom

Leibensperger, Eric, Department of Physics and Astronomy, Ithaca College, Ithaca,
New York

Lems, Johanna, Department of Geodesy and Geoinformation, TU Wien, Vienna,
Austria

Lennard, Chris, Department of Environmental and Geographical Science, University
of Cape Town, Cape Town, South Africa

Levenson, Eric S., University of Oregon, Eugene, Oregon

Liley, Ben, National Institute of Water and Atmospheric Research (NIWA), Lauder,
New Zealand

Lo, Y. T. Eunice, Cabot Institute for the Environment; University of Bristol, Bristol,
United Kingdom; Elizabeth Blackwell Institute for Health Research, University of
Bristol, Bristol, United Kingdom

Loeb, Norman G., NASA Langley Research Center, Hampton, Virginia

Loyola, Diego, German Aerospace Center (DLR), Oberpfaffenhofen, Germany

Macara, Gregor, National Institute of Water and Atmospheric Research (NIWA),
Lauder, New Zealand

Magnin, Florence, Laboratoire EDYTEM, CNRS/Université Savoie Mont-Blanc, Le
Bourget-du-Lac, France

Matsuzaki, Shin-Ichiro, National Institute for Environmental Studies, Tsukuba,
Japan

Matthews, Tom, Department of Geography, King's College London, London, United
Kingdom

Mayer, Michael, Department of Meteorology and Geophysics, University of Vienna,
Vienna, Austria; Research Department, European Centre for Medium-Range
Weather Forecasts, Bonn, Germany
Editor and Author Affiliations (continued)

McVicar, Tim R., CSIRO Environment, Canberra, Australia; Australian Research Council Centre of Excellence for Climate Extremes, Sydney, Australia

Mears, Carl A., Remote Sensing Systems, Santa Rosa, California

Menzel, Annette, Ecoclimatology, Department of Life Science Systems, TUM School of Life Sciences, Technical University of Munich, Freising, Germany; Institute for Advanced Study, Technical University of Munich, Garching, Germany

Merchant, Christopher J., University of Reading, Reading, United Kingdom

Meyer, Michael F., U.S. Geological Survey, Madison, Wisconsin

Miralles, Diego G., Hydro-Climate Extremes Lab (H-CEL), Ghent University, Ghent, Belgium

Montzka, Stephan A., NOAA Global Monitoring Laboratory, Boulder, Colorado

Morice, Colin, Met Office Hadley Centre, Exeter, United Kingdom

Morino, Isamu, National Institute for Environmental Studies Tsukuba, Ibaraki, Japan

Morris, Gary A., NOAA Global Monitoring Laboratory, Boulder, Colorado

Mrekaj, Ivan, Technical University in Zvolen, Zvolen, Slovakia

Mühle, Jens, Scripps Institution of Oceanography, San Diego, California

Nance, D., Cooperative Institute for Research in Environmental Sciences, University of Colorado Boulder, Boulder, Colorado

Nicolas, Julien P., European Centre for Medium-Range Weather Forecasts, Bonn, Germany

Noetzli, Jeannette, WSL Institute for Snow and Avalanche Research SLF, Davos Dorf, Switzerland; Climate Change, Extremes and Natural Hazards in Alpine Regions Research Centre (CERC), Davos Dorf, Switzerland

O'Keefe, John, The Harvard Forest, Harvard University, Petersham, Massachusetts

Ollinik, Jessica Erin, Department of Chemistry and Biochemistry, University of Regina, Regina, Canada

Osborn, Timothy J., Climatic Research Unit, School of Environmental Sciences, University of East Anglia, Norwich, United Kingdom

Parrington, Mark, European Centre for Medium-Range Weather Forecasts, Bonn, Germany

Pellet, Cécile, Department of Geosciences, University of Fribourg, Fribourg, Switzerland

Pelto, Mauri, Nichols College, Dudley, Massachusetts

Pennington, Elyse, Jet Propulsion Laboratory, California Institute of Technology, Pasadena, California

Petersen, Kyle, Cooperative Institute for Research in Environmental Sciences, University of Colorado Boulder, Boulder, Colorado; NOAA Global Monitoring Laboratory, Boulder, Colorado

Phillips, Coda, Cooperative Institute for Meteorological Satellite Studies, Space Science and Engineering Center, University of Wisconsin-Madison, Madison, Wisconsin

Pierson, Don, Department of Ecology and Genetics, Uppsala University, Uppsala, Sweden

Pinto, Izidine, Royal Netherlands Meteorological Institute (KNMI), De Bilt, The Netherlands

Po-Chedley, Stephen, Lawrence Livermore National Laboratory, Livermore, California

Pogliotti, Paolo, Environmental Protection Agency of Valle d'Aosta, Saint Christophe, Italy

Polvani, Lorenzo, Columbia University, New York, New York

Preimesberger, Wolfgang, Department of Geodesy and Geoinformation, TU Wien, Vienna, Austria

Price, Colin, Tel Aviv University, Tel Aviv, Israel

Pulkkanen, Merja, Finnish Environment Institute (SYKE), Helsinki, Finland

Randel, William J., National Center for Atmospheric Research, Boulder, Colorado

Raymond, Colin, Joint Institute for Regional Earth System Science and Engineering, University of California, Los Angeles, Los Angeles, California

Remy, Samuel, HYGEOS, Lille, France

Ricciardulli, Lucrezia, Remote Sensing Systems, Santa Rosa, California

Richardson, Andrew D., School of Informatics, Computing, and Cyber Systems, Northern Arizona University, Flagstaff, Arizona; Center for Ecosystem Science and Society, Northern Arizona University, Flagstaff, Arizona

Robinson, David A., Rutgers University, Piscataway, New Jersey

Rodell, Matthew, Earth Sciences Division, NASA Goddard Space Flight Center, Greenbelt, Maryland

Rodriguez-Fernandez, Nemesio, CESBIO, Université de Toulouse, CNES/CNRS/ INRAe/IRD/UPS, Toulouse, France

Rogers, Cassandra D.W., Bureau of Meteorology, Melbourne, Australia

Rohini, P., India Meteorological Department, Ministry of Earth Sciences, Pune, India

Rosenlof, Karen H., NOAA Chemical Sciences Laboratory, Boulder, Colorado

Rozanov, Alexei, University of Bremen, Bremen, Germany

Rozkošný, Jozef, Slovak Hydrometeorological Institute, Bratislava, Slovakia

Rusanovskaya, Olga O., Institute of Biology, Irkutsk State University, Irkutsk, Russia

Rutishauser, This, Swiss Academy of Sciences (SCNAT), Bern, Switzerland

Sabeerali, C. T., India Meteorological Department, Ministry of Earth Sciences, Pune, India

Sakai, Tetsu, Meteorological Research Institute, Japan Meteorological Agency, Tsukuba, Ibaraki, Japan

Salamon, Peter, Joint Research Centre, European Commission, Ispra, Italy

Sánchez-Lugo, Ahira, NOAA/NESDIS National Centers for Environmental Information, Asheville, North Carolina

Sawaengphokhai, Parnchai, ADNET Systems, Inc., Hampton, Virginia

Schenzinger, Verena, Medical University of Innsbruck, Innsbruck, Austria

Schmid, Martin, Eawag, Department Surface Waters—Research and Management, Kastanienbaum, Switzerland

Sezaki, Fumi, Japan Meteorological Agency, Tokyo, Japan

Shao, Xi, Earth System Science Interdisciplinary Center, University of Maryland, College Park, Maryland

Sharma, Sapna, Department of Biology, York University, Toronto, Ontario, Canada

Shi, Lei, NOAA/NESDIS National Centers for Environmental Information, Asheville, North Carolina

Shimaraeva, Svetlana V., Institute of Biology, Irkutsk State University, Irkutsk, Russia

Shinohara, Ryuichiro, Regional Environment Conservation Division, National Institute for Environmental Studies, Tsukuba, Ibaraki, Japan

Silow, Eugene A., Institute of Biology, Irkutsk State University, Irkutsk, Russia

Simmons, Adrian J., European Centre for Medium-Range Weather Forecasts, Reading, United Kingdom

Smith, Katie, Cooperative Institute for Research in Environmental Sciences, University of Colorado Boulder, Boulder, Colorado; NOAA Chemical Sciences Laboratory, Boulder, Colorado

Smith, Sharon L., Geological Survey of Canada, Natural Resources Canada, Ottawa, Canada

Soden, Brian J., University of Miami, Miami, Florida

Sofieva, Viktoria, Finnish Meteorological Institute, Helsinki, Finland

Soldo, Logan, Cooperative Institute for Research in Environmental Sciences, University of Colorado Boulder, Boulder, Colorado; NOAA Global Monitoring Laboratory, Boulder, Colorado

Sreejith, O. P., India Meteorological Department, Ministry of Earth Sciences, Pune, India

Stackhouse, Jr., Paul W., NASA Langley Research Center, Hampton, Virginia

Stauffer, Ryan M., Atmospheric Chemistry and Dynamics Laboratory, NASA Goddard Space Flight Center, Greenbelt, Maryland

Steinbrecht, Wolfgang, Deutscher Wetterdienst, Hohenpeissenberg, Germany

Steiner, Andrea K., Wegener Center for Climate and Global Change, University of Graz, Graz, Austria

Stevens, Thea, Department of Meteorology, University of Reading, Reading, United Kingdom
Editor and Author Affiliations (continued)

Stoy, Paul C., College of Agricultural and Life Sciences, University of Wisconsin-
Madison, Madison, Wisconsin

Streletskiy, Dmitry A., George Washington University, Washington, DC

Taha, Ghassan, Morgan State University, Baltimore, Maryland; NASA Goddard Flight
Center, Greenbelt, Maryland

Thackeray, Stephen J., United Kingdom Centre for Ecology & Hydrology, Lancaster, United Kingdom

Thibert, Emmanuel, Institut de Géosciences de l'Environnement (IGE), Université Grenoble Alpes, INRAE, CNRS, IRD, Grenoble INP, IGE, Grenoble, France

Timofeyev, Maxim A., Institute of Biology, Irkutsk State University, Irkutsk, Russia

Tourpali, Kleareti, Aristotle University, Thessaloniki, Greece

Tronquo, Emma, Hydro-Climate Extremes Lab (H-CEL), Ghent University, Ghent, Belgium; Department of Geoscience and Remote Sensing, TU Delft, Delft, The Netherlands

Tye, Mari R., National Center for Atmospheric Research, Boulder, Colorado

Urraca, Ruben, Joint Research Centre, European Commission, Ispra, Italy

van der A, Ronald, Royal Netherlands Meteorological Institute (KNMI), DeBilt, The Netherlands

van der Schrier, Gerard, Royal Netherlands Meteorological Institute (KNMI), DeBilt,
The Netherlands

Van Scoy, Greta, The Harvard Forest, Harvard University, Petersham, Massachusetts

van Vliet, Arnold J. H., Earth Systems and Global Change Group, Wageningen
University & Research, The Netherlands

Veal, Karen, National Centre for Earth Observation (NCEO), University of Leicester, Leicester, United Kingdom; School of Physics and Astronomy, University of Leicester, Leicester, United Kingdom

Verburg, Piet, National Institute of Water and Atmospheric Research (NIWA), Hamilton, New Zealand

Vernier, Jean-Paul, NASA Langley Research Center, Hampton, Virginia

Vimont, Isaac J., NOAA Global Monitoring Laboratory, Boulder, Colorado

Viticchie, Bartolomeo, EUMETSAT, Darmstadt, Germany

Vivero, Sebastián, Department of Geosciences, University of Fribourg, Fribourg, Switzerland

Vömel, Holger, National Center for Atmospheric Research, Boulder, Colorado

Vose, Russell S., NOAA/NESDIS National Centers for Environmental Information, Asheville, North Carolina

Wang, Donqian, Climate Change Division, National Climate Center, China
Meteorological Administration, Beijing, China

Wang, Ray H. J., Georgia Institute of Technology, Atlanta, Georgia

Waring, Abigail Marie, National Centre for Earth Observation (NCEO), University of Leicester, Leicester, United Kingdom; School of Physics and Astronomy, University of Leicester, Leicester, United Kingdom

Warnock, Taran, University of Saskatchewan, Saskatoon, Canada

Weber, Mark, University of Bremen, Bremen, Germany

Wei, Zigang, Science and Technology Corporation (STC), College Park, Maryland

Wiese, David N., Jet Propulsion Laboratory, California Institute of Technology, Pasadena, California

Wild, Jeannette D., Earth System Science Interdisciplinary Center, University of Maryland, College Park, Maryland; NOAA/NESDIS Center for Satellite Applications and Research, College Park, Maryland

Willett, Kate M., Met Office Hadley Centre, Exeter, United Kingdom

Williams, Earle, Massachusetts Institute of Technology, Cambridge, Massachusetts

Wong, Takmeng, NASA Langley Research Center, Hampton, Virginia

Wood, Tom, Department of Geography, King's College London, London, United Kingdom

Woolway, Richard Iestyn, School of Ocean Sciences, Bangor University, Bangor, United Kingdom

Worden, John, Jet Propulsion Laboratory, California Institute of Technology, Pasadena, California

Yang, Kai, Department of Atmospheric and Oceanic Sciences, University of Maryland, College Park, Maryland

Yin, Xungang, NOAA/NESDIS National Centers for Environmental Information, Asheville, North Carolina

Zeng, Zhenzhong, School of Environmental Science and Engineering, Southern University of Science and Technology, Shenzhen, China

Zhao, Lin, School of Geographical Sciences, Nanjing University of Information
Science and Technology, Nanjing, China

Ziemke, Jerry R., Goddard Earth Sciences Technology and Research, Morgan State University, Baltimore, Maryland; NASA Goddard Space Flight Center, Greenbelt, Maryland

Ziese, Markus, Global Precipitation Climatology Centre, Deutscher Wetterdienst, Offenbach, Germany

Zotta, Ruxandra-Maria, TU Wien, Vienna, Austria

Zou, Cheng-Zhi, George Mason University, Fairfax, Virginia

Editorial and Production Team

Allen, Jessica, Graphics Support, Cooperative Institute for Satellite Earth System Studies, North Carolina State University, Asheville, North Carolina

Camper, Amy V., Graphics Support, Innovative Consulting and Management Services, LLC, NOAA/NESDIS National Centers for Environmental Information, Asheville, North Carolina

Carroll, Lauren, Content Team Lead, Communications and Outreach, NOAA/NESDIS National Centers for Environmental Information, Asheville, North Carolina

Haley, Bridgette O., Graphics Support, NOAA/NESDIS National Centers for Environmental Information, Asheville, North Carolina

Love-Brotak, S. Elizabeth, Lead Graphics Production, NOAA/NESDIS National Centers for Environmental Information, Asheville, North Carolina

Ohlmann, Laura, Technical Editor, Innovative Consulting and Management Services, LLC, NOAA/NESDIS National Centers for Environmental Information, Asheville, North Carolina

Noguchi, Lukas, Technical Editor, Innovative Consulting and Management Services, LLC, NOAA/NESDIS National Centers for Environmental Information, Asheville, North Carolina

Riddle, Deborah B., Graphics Support, NOAA/NESDIS National Centers for Environmental Information, Asheville, North Carolina

Veasey, Sara W., Visual Communications Team Lead, Communications and Outreach, NOAA/NESDIS National Centers for Environmental Information, Asheville, North Carolina
## 2. Table of Contents

List of authors and affiliations...................................................S14

a. Overview...................................................................................S20

b. Temperature...................................................................................S28

1. Surface temperature...................................................................S28

2. Lake surface temperature...................................................................S29

3. Night marine air temperature................................................................S31

4. Surface temperature extremes...................................................S32

Sidebar 2.1: Super extreme land surface temperature hotspots...................................................S36

5. Tropospheric temperature...................................................................S39

6. Stratospheric temperature...................................................................S41

7. Equivalent temperature...................................................................S42

c. Cryosphere...................................................................................S46

1. Permafrost temperature and active-layer thickness...................................................S46

2. Rock glacier velocity...........................................................................S48

3. Alpine glaciers...................................................................................S50

4. Lake ice cover...................................................................................S51

5. Northern Hemisphere continental snow cover extent...................................................S53

d. Hydrological cycle (atmosphere)...................................................S55

1. Surface humidity...........................................................................S55

2. Humid-heat extremes over land...................................................S58

3. Total column water vapor...................................................................S61

4. Upper-tropospheric humidity...................................................S63

5. Precipitation...................................................................................S64

6. Land surface precipitation extremes...................................................S66

7. Cloudiness...................................................................................S67

d. Hydrological cycle (land)...........................................................S70

8. Lake water storage and level...................................................S70

9. River discharge and runoff...........................................................S71

10. Groundwater and terrestrial water storage...........................................S73

11. Soil moisture...................................................................................S75

12. Monitoring drought using the self-calibrating Palmer Drought Severity Index...................S77

13. Land evaporation...........................................................................S78

e. Atmospheric circulation.................................................................S80

1. Mean sea level pressure and related modes of variability...................................................S80

2. Land and ocean surface winds...........................................................S81

3. Upper-air winds...................................................................................S83

4. Novel lightning flash densities from space...........................................S85

f. Earth radiation budget.................................................................S87

1. Earth radiation budget at top-of-atmosphere...................................................S87

2. Mauna Loa apparent transmission record update for 2024...........................................S89
## 2. Table of Contents

<table><tr><td><strong>g. Atmospheric composition</strong></td><td>S91</td></tr><tr><td>&nbsp;&nbsp;&nbsp;&nbsp;1. Long-lived greenhouse gases</td><td>S91</td></tr><tr><td>&nbsp;&nbsp;&nbsp;&nbsp;2. Ozone-depleting gases</td><td>S94</td></tr><tr><td>&nbsp;&nbsp;&nbsp;&nbsp;3. Tropospheric aerosols</td><td>S95</td></tr><tr><td>&nbsp;&nbsp;&nbsp;&nbsp;4. Tropospheric ozone</td><td>S97</td></tr><tr><td>&nbsp;&nbsp;&nbsp;&nbsp;5. Stratospheric aerosols</td><td>S99</td></tr><tr><td>&nbsp;&nbsp;&nbsp;&nbsp;6. Stratospheric ozone</td><td>S102</td></tr><tr><td>&nbsp;&nbsp;&nbsp;&nbsp;7. Stratospheric water vapor</td><td>S104</td></tr><tr><td>&nbsp;&nbsp;&nbsp;&nbsp;8. Carbon monoxide</td><td>S106</td></tr><tr><td><strong>Sidbar 2.2: Operational satellite instruments monitor a range of indirect short-lived climateforcers</strong></td><td>S108</td></tr><tr><td><strong>h. Land surface properties</strong></td><td>S111</td></tr><tr><td>&nbsp;&nbsp;&nbsp;&nbsp;1. Terrestrial surface albedo dynamics</td><td>S111</td></tr><tr><td>&nbsp;&nbsp;&nbsp;&nbsp;2. Terrestrial vegetation dynamics</td><td>S112</td></tr><tr><td>&nbsp;&nbsp;&nbsp;&nbsp;3. Biomass burning</td><td>S114</td></tr><tr><td>&nbsp;&nbsp;&nbsp;&nbsp;4. Phenology</td><td>S116</td></tr><tr><td>&nbsp;&nbsp;&nbsp;&nbsp;5. Vegetation optical depth</td><td>S120</td></tr><tr><td><strong>Acknowledgments</strong></td><td>S122</td></tr><tr><td><strong>Appendix 1: Acronyms</strong></td><td>S128</td></tr><tr><td><strong>Appendix 2: Datasets and sources</strong></td><td>S135</td></tr><tr><td><strong>Appendix 3: Supplemental materials</strong></td><td>S148</td></tr><tr><td><strong>References</strong></td><td>S157</td></tr></table>
## 2. GLOBAL CLIMATE

R. J. H. Dunn, J. Blannin, K. M. Willett, N. Gobron, and G. A. Morris, Eds.

### a. Overview

—R. J. H. Dunn, J. Blannin, K. M. Willett, N. Gobron, and G. A. Morris

For the second year in a row, record-high global surface temperatures were set in 2024, according to all six global temperature datasets assessed in this report (Berkeley Earth, GISTEMP, HadCRUT5, the NOAA Merged Land Ocean Global Surface Temperature Analysis [NOAAGlobalTemp], ERA5, and the Japanese Reanalysis for Three Quarters of a Century [JRA-3Q]). The last time consecutive years set records was in 2015 and 2016 when a strong El Niño similarly boosted global temperatures. The last 10 years (2015–24) are now the warmest 10 in the instrumental record—warmer than the 2011–20 average—and hence “more likely than not warmer than any multi-century period after the last interglacial period, roughly 125,000 years ago” (Gulev et al. 2021). The increased energy within the climate system is detectable at the top of the atmosphere, with the outgoing longwave radiation anomaly continuing to be above the range of natural variability.

During 2024, El Niño conditions that had been present since the middle of 2023 faded to neutral by the end of the year. The warm conditions observed around the globe over the last two years had impacts across the climate system, as demonstrated by many of the metrics presented in this chapter. Other temperature metrics also reached record levels over the instrumental periods assessed in this chapter: over the oceans at night, on the surfaces of lakes, and in the lower troposphere as well as measures of equivalent temperature (which considers the moisture contribution to heat), and high and low temperature extremes.

The frozen parts of Earth responded with permafrost temperatures continuing to reach record-high levels in many locations, and the active-layer thickness (the portion that melts and refreezes annually) also increasing at most sites. Repeated high temperatures over the European Alps during recent summers has led to large increases in rock glacier velocities in that region. The Great Lakes had much-below-average ice cover over the 2023/24 winter, and there was below-average snow cover extent in the Northern Hemisphere. All 58 reference glaciers across five continents lost ice during 2024, resulting in the greatest average ice loss in the record, which began in 1970. One more glacier was also declared extinct during 2024.

Higher global temperatures impacted the water cycle. Although lower than 2023 values, water evaporation from land in the Northern Hemisphere reached one of the highest annual values on record, in line with the long-term increasing trend. Specific humidity reached record levels over land and ocean, and relative humidity over both domains was higher than 2023. There was little relief from high humid-heat conditions, with the frequency of high humid-heat days at a record level and intensity at the second-highest level in the record—only a fraction of a degree cooler than that of 2023. The global atmosphere contained the greatest amount of water vapor in the record, and over one-fifth of the globe recorded their highest values. This far exceeded 2023, where only one-tenth of the globe experienced record-high total column water vapor. Rainfall was globally high; 2024 was the third-wettest year since records began in 1983. However, rainfall over land was close to average, while over the ocean it was the fourth-wettest year on record (following 2015, 2016, and 1998). Extreme rainfall, as characterized by the annual maximum daily rainfall over land, was the wettest on record. Averaged globally (4190 lakes), lakes had a small increase in water storage, and regionally, over 40% of monitored lakes showed significant changes in storage and level.
The effects of ongoing droughts in southern Africa and in North and South America can be seen in the soil moisture and water storage patterns. They are also apparent in the river discharge and runoff levels, which are topics that will be covered in the chapter after a few years of absence. Globally, however, drought severity and extent decreased from the record set in 2023.

Atmospheric concentrations of the three main greenhouse gases (carbon dioxide [CO₂], methane [CH₄], nitrous oxide [N₂O]) again all reached record levels, with a record-equal annual increase in the annual change of CO₂ concentrations. However, concentrations of ozone-depleting substances continued to decline, corroborated by stratospheric ozone columns well above the 1998–2008 average, especially in the Northern Hemisphere. In contrast, stratospheric aerosols remained high because of the Ruang eruption in April 2024, affecting the atmospheric transmission of solar radiation over Hawaii later in the year, and the ongoing effects from the Hunga eruption in 2022. The latter eruption also caused the ongoing elevated stratospheric water vapor concentrations.

Our planet's surface albedo continued to darken with increased plant growth and decreased snow and ice cover. Plants responded to the warmer temperatures with some of the earliest starts to spring in the record over Europe—one to two weeks earlier than the 2000–20 baseline—and a warm autumn resulted in a much longer leaf-on season. Severe wildfire seasons occurred in South America (the worst since 2010), Canada (for the second consecutive year), and the Arctic, contributing to the second-highest atmospheric carbon monoxide concentrations since 2003 and the highest tropospheric aerosol optical depth since 2019, at 550 nm.

This year's iteration of the Global Climate chapter features two Sidebars, both of which present new topics that have not yet been explored in the report. The first covers the ability of satellite products to monitor changes in land surface temperature extremes and identify hotspots where regions of Earth are becoming uninhabitable. This Sidebar also discusses the importance of dataset stability for climate studies, as well as the correlation of land surface temperature and air temperature anomalies. The second Sidebar complements the section on greenhouse gas concentrations by examining short-lived climate forcers—compounds that have lifetimes ranging from a few hours to a few decades.

As usual in the Global Climate chapter, Plate 2.1 shows maps of global annual anomalies for many of the variables and metrics presented herein. Many of these variables are also presented as time series in Plate 1.1. Most sections now use the 1991–2020 climatological reference period, in line with the World Meteorological Organization's (WMO) recommendations, although this reference period is not possible for all datasets due to their length or legacy processing methods.
<figure><img src="image_4.png" /><figcaption>(b) Lake Temperature</figcaption></figure>

<figure><img src="image_5.png" /><figcaption>(c) Night Marine Air Temperature</figcaption></figure>

<figure><img src="image_6.png" /><figcaption>(d) Warm Days (TX90p)</figcaption></figure>

<figure><img src="image_7.png" /><figcaption>(e) Cool Nights (TN10p)</figcaption></figure>

<figure><img src="image_8.png" /><figcaption>(f) Lower-Tropospheric Temperature</figcaption></figure>

<figure><img src="image_9.png" /><figcaption>(g) Equivalent Temperature</figcaption></figure>

<figure><img src="image_10.png" /><figcaption>Plate 2.1. (a) NOAA NCEI Global land and ocean surface annual temperature anomalies (°C); (b) Satellite-derived lake surface water temperature annual anomalies, from European Space Agency (ESA) Climate Change Initiative (CCI) LAKES/Copernicus Climate Change Service (C3S) / Earth Observation Climate Information Service (EOCIS) (°C); (c) Climate Linked Atlantic Sector Science Night Marine Air Temperature (CLASSnmat) night marine air temperature annual average anomalies (°C); (d) ERA5 warm day threshold exceedance (TX90p); (e) ERA5 cool night threshold exceedance (TN10p); (f) Average of Remote Sensing Systems (RSS) and University of Alabama in Huntsville (UAH) lower-tropospheric annual temperature anomalies (°C). Hatching denotes regions in which 2024 was the warmest year on record; (g) ERA5 annual equivalent temperature anomalies (°C);</figcaption></figure>
<figure><img src="image_11.png" /></figure>

<figure><img src="image_12.png" /></figure>

<figure><img src="image_13.png" /></figure>

<figure><img src="image_14.png" /></figure>

<figure><img src="image_15.png" /></figure>

<figure><img src="image_16.png" /></figure>

<figure><img src="image_17.png" /><figcaption>Plate 2.1 (cont.) (h) ERA5 surface specific humidity anomalies (g kg⁻¹); (i) ERA5 surface relative humidity anomalies (%rh); (j) Met Office Hadley Centre International Surface Dataset of Humidity extremes (HadISDH/extremes) humid heat intensity (T<sub>w</sub>X), measured by the annual median anomaly of daily maximum wet-bulb temperature (°C). Gray background (over land) represent regions with insufficient data; (k) HadISDH/extremes humid heat frequency anomalies (T<sub>w</sub>X90p), measured by the number of days where the daily maximum wet-bulb temperature exceeds the local daily 90th percentile (days yr⁻¹). Gray background (over land) represent regions with insufficient data; (l) JRA-3Q Total column water vapor (TCWV) anomalies (%). Data from Global Navigation Satellite System (GNSS) stations are plotted as filled circles; (m) Annual microwave-based upper-tropospheric humidity (UTH) anomalies (%rh); (n) Global Precipitation Climatology Centre (GPCC) annual mean precipitation anomalies (mm yr⁻¹);</figcaption></figure>
<figure><img src="image_18.png" /><figcaption>(q) Lake Water Storage</figcaption></figure>

<figure><img src="image_19.png" /><figcaption>(s) River Discharge</figcaption></figure>

<figure><img src="image_20.png" /><figcaption>(u) Terrestrial Water Storage</figcaption></figure>

<figure><img src="image_21.png" /><figcaption>(p) Cloudiness</figcaption></figure>

<figure><img src="image_22.png" /><figcaption>(r) Runoff</figcaption></figure>

<figure><img src="image_23.png" /><figcaption>(t) Terrestrial Water Storage Difference</figcaption></figure>

<figure><img src="image_24.png" /></figure>

Plate 2.1 (cont.) (o) GPCC maximum five-day (Rx5day) annual precipitation anomalies (mm); (p) PATMOS-x 6.0 cloud fraction annual anomalies (%); (q) GloLakes lake water storage anomalies (%); (r) Global Flood Awareness System version 4 (GloFASv4) runoff anomalies (mm yr⁻¹); (s) GloFASv4 river discharge anomalies (m³ s⁻¹); (t) Gravity Recovery and Climate Experiment Follow-On (GRACE-FO) difference in annual-mean terrestrial water storage between 2023 and 2024 (cm); (u) GRACE-FO terrestrial water storage anomalies (cm);
<figure><img src="image_25.png" /><figcaption>(x) Land Evaporation</figcaption></figure>

<figure><img src="image_26.png" /><figcaption>(z) Surface Winds</figcaption></figure>

<figure><img src="image_27.png" /><figcaption>(ab) Total Aerosol Optical Depth</figcaption></figure>

<figure><img src="image_28.png" /><figcaption>(w) Drought (self-calibrating PDSI)</figcaption></figure>

<figure><img src="image_29.png" /><figcaption>(y) Sea Level Pressure</figcaption></figure>

<figure><img src="image_30.png" /><figcaption>(aa) Upper-Air (850 hPa) Eastward Winds</figcaption></figure>

<figure><img src="image_31.png" /></figure>

Plate 2.1 (cont.) (v) Copernicus Climate Change Service (C3S) average surface soil moisture anomalies (m³ m⁻³). Data are masked where no retrieval is possible or where the quality is not assured and flagged, for example due to dense vegetation, frozen soil, or radio frequency interference; (w) Mean self-calibrating Palmer Drought Severity Index (scPDSI). Droughts are indicated by negative values (brown), wet episodes by positive values (green). No calculation is made where a drought index is meaningless (gray areas: ice sheets or deserts with approximately zero mean precipitation); (x) Great Lakes Environmental Assessment and Mapping Project (GLEAM) land evaporation anomalies (mm yr⁻¹); (y) ERA5 mean sea level pressure anomalies (hPa); (z) Surface wind speed anomalies (m s⁻¹) from the observational HadISD3 dataset (land, circles), the ERA5 reanalysis output (land, shaded areas), and RSS satellite observations (ocean, shaded areas); (aa) ERA5 850-hPa eastward wind speed anomalies for Oct–Dec (m s⁻¹); (ab) Copernicus Atmosphere Monitoring Service (CAMS) reanalysis total aerosol optical depth (AOD) anomalies at 550 nm;
<figure><img src="image_32.png" /><figcaption>(ae) OMI/MLS Tropospheric Column Ozone</figcaption></figure>

<figure><img src="image_33.png" /><figcaption>(ag) Carbon Monoxide</figcaption></figure>

<figure><img src="image_34.png" /><figcaption>(ai) Near Infrared Land Surface Albedo</figcaption></figure>

<figure><img src="image_35.png" /><figcaption>(ad) Extreme Aerosol Days</figcaption></figure>

<figure><img src="image_36.png" /><figcaption>(af) Stratospheric (Total Column) Ozone</figcaption></figure>

<figure><img src="image_37.png" /><figcaption>(ah) Visible Land Surface Albedo</figcaption></figure>

<figure><img src="image_38.png" /></figure>

Plate 2.1 (cont.) (ac) CAMS reanalysis PM2.5 anomalies (µg m⁻³); (ad) Number of days with AOD above the 99.9th percentile from CAMS reanalysis. Areas with zero days appear as the white/gray background; (ae) Ozone Monitoring Instrument (OMI)/Microwave Limb Sounder (MLS) tropospheric ozone column anomalies for 60°S-60°N (DU); (af) total column ozone anomalies determined from Tropospheric Monitoring Instrument (TROPOMI) aboard Sentinel-5 Precursor (S5P; DU); (ag) CAMS reanalysis total column carbon monoxide anomalies (x 10¹⁸ molecules cm⁻²); (ah) Visible Infrared Imaging Radiometer Suite (VIIRS) land surface visible broadband albedo anomalies (%); (ai) VIIRS land surface near-infrared albedo anomalies (%);
<figure><img src="image_39.png" /><figcaption>(ak) Fraction of Absorbed Photosynthetically Active Radiation</figcaption></figure>

<figure><img src="image_40.png" /><figcaption>(al) Carbon Emissions from Biomass Burning</figcaption></figure>

<figure><img src="image_41.png" /><figcaption>(am) Vegetation Optical Depth</figcaption></figure>

<figure><img src="image_42.png" /></figure>

Plate 2.1 (cont.) (aj) VIIRS land surface shortwave broadband albedo anomalies (%); (ak) Fraction of absorbed photosynthetically active radiation (FAPAR) anomalies; (al) Global Fire Assimilation System version 1.4 (GFASv1.4) carbonaceous emission anomalies (g C m⁻² yr⁻¹) from biomass burning; (am) Vegetation Optical Depth Climate Archive (VODCA) CXKu-band vegetation optical depth (VOD) anomalies.
## b. Temperature

### 1. SURFACE TEMPERATURE

—A. Arguez, A. Bunno, A. Goto, C. Morice, J. P. Nicolas, A. Sánchez-Lugo, and F. Sezaki

For the second consecutive year, a new global surface temperature record was set. According to six global temperature datasets, the global surface temperature for 2024 was 0.63°C–0.72°C above the 1991–2020 average, (Table 2.1; Fig. 2.1). This was the highest value since global records began in the mid-1800s to mid-1900s, surpassing the previous warmest year on record set only last year (2023) by a margin of +0.08°C to +0.12°C.

According to all six global datasets, the last 10 years (2015–24) were the 10 warmest years on record. The datasets consist of four global in situ surface temperature analyses (GISTEMP, Lenssen et al. 2019; HadCRUT5, Morice et al. 2021; the NOAA Merged Land Ocean Global Surface Temperature Analysis [NOAAGlobalTemp], Vose et al. 2021; Berkeley Earth, Rhode and Hausfather 2020) and two global atmospheric reanalyses (ERA5, Hersbach et al. 2020, Soci et al. 2024; the Japanese Reanalysis for Three Quarters of a Century [JRA-3Q], Kosaka et al. 2024).

The global surface temperature for 2024 was also 1.46°C–1.62°C above the 1850–1900 average (a period commonly used to represent pre-industrial conditions). The pre-industrial temperature anomaly range was computed using the three datasets that extend back to 1850 (NOAAGlobalTemp, HadCRUT5, Berkeley Earth) using each dataset's own 1850–1900 baseline. Two of the three datasets indicated that the yearly temperature anomaly surpassed +1.5°C, the most ambitious limit set by the Paris Agreement (Paris Agreement 2015). Of note, exceeding +1.5°C in a single year does not represent a failure to achieve the Paris Agreement limit; this would require breaching +1.5°C over a longer period (WMO 2025a).

The global trends are within 0.20°C–0.22°C decade⁻¹ for the short-term (1981–2024) and are within 0.08°C–0.09°C decade⁻¹ for the long-term (1880–2024). Following the Arguez et al. (2020) approach, 2024 was 0.23°C–0.29°C above the value derived from the linear trend

<figure><img src="image_43.png" /><figcaption>Fig. 2.1. Global average surface air temperature anomalies (°C; 1991–2020 base period). In situ estimates are shown from the NOAA Merged Land Ocean Global Surface Temperature Analysis (NOAAGlobalTemp; Vose et al. 2021), NASA Goddard Institute for Space Studies Surface Temperature Analysis version 4 (GISTEMPv4; Lenssen et al. 2019), Hadley Centre/Climatic Research Unit Temperature version 5 (HadCRUT5; Morice et al. 2021), Climatic Research Unit temperature version 5 (CRUTEM5; Osborn et al. 2021), Hadley Centre Sea Surface Temperature Dataset version 4 (HadSST4; Kennedy et al. 2019), and Berkeley Earth (Rhode and Hausfather 2020). Reanalysis estimates are shown from ERA5 (Hersbach et al. 2020; Bell et al. 2021) and the Japanese Reanalysis for Three Quarters of a Century (JRA-3Q; Kosaka et al. 2024).</figcaption></figure>

<table><caption>Table 2.1. Global temperature anomalies (°C; 1991–2020 base period) for 2024. Note that for the HadCRUT5 column, land values were computed using the Climatic Research Unit Temperature version 5 (CRUTEM.5.0.2.0) dataset (Osborn et al. 2021), ocean values were computed using the Hadley Centre Sea Surface Temperature Dataset version 4 (HadSST.4.0.1.0) dataset (Kennedy et al. 2019), and global land and ocean values were computed using the HadCRUT.5.0.2.0 dataset (Morice et al. 2021).</caption><thead><tr><th>Global</th><th>NASA-GISTEMPv4</th><th>HadCRUT5</th><th>NOAA GlobalTemp</th><th>Berkeley Earth</th><th>ERA5</th><th>JRA-3Q</th></tr></thead><tbody><tr><td>Land</td><td>+0.98</td><td>+0.89</td><td>+1.04</td><td>+0.94</td><td>+1.06</td><td>+1.01</td></tr><tr><td>Ocean</td><td>+0.50</td><td>+0.50</td><td>+0.49</td><td>-</td><td>+0.58</td><td>+0.56</td></tr><tr><td>Land and Ocean</td><td>+0.67</td><td>+0.63</td><td>+0.67</td><td>+0.65</td><td>+0.72</td><td>+0.69</td></tr></tbody></table>
calculated over the last 50 years (1975–2024), registering the highest departures above the trend lines in all six global datasets.

The annual global land-only and ocean-only surface temperatures were also record high, at 0.89°C–1.06°C and 0.49°C–0.58°C above the 1991–2020 average, respectively (Table 2.1). The year was characterized by much-warmer-than-average conditions across most of the world's surface (Plate 2.1a; Appendix Figs. A2.1–A2.4), with record-high annual temperatures observed across parts of each continent and across large areas in the North and tropical Atlantic Ocean, North Indian Ocean, western Pacific Ocean, Arctic Ocean, and the Southern Ocean. In contrast, below-average annual temperatures were observed across Iceland, southern Greenland, the Bering Sea, the Okhotsk Sea, and parts of the eastern South Pacific, Southern Ocean, and Antarctica.

Monthly global surface temperatures were exceptionally high throughout the year, with each month ranking either as the warmest or the second-warmest on record. A strong El Niño event, which began during boreal summer 2023, continued into early 2024 before ending in boreal spring. The ensuing El Niño–Southern Oscillation (ENSO)-neutral conditions persisted through the remainder of the year until La Niña-like conditions emerged at the end of 2024. While several factors may have contributed to the record-high temperature in 2024, the influence of El Niño together with unusually warm oceans across many basins were key contributors to the high monthly global surface temperature records observed, especially during the first half of the year, adding warmth on top of the long-term warming caused by anthropogenic greenhouse gas emissions. The last time at least two consecutive years reached a new global surface temperature record was in 2015 and 2016, when a strong El Niño developed during the latter half of 2015 and dissipated by May 2016.

## 2. LAKE SURFACE TEMPERATURE

—L. Carrea, C. J. Merchant, R. I Woolway, J.-F. Creatux, T. M. Dokulil, H. Dugan, A. Laas, E. Leibensperger, S.-I. Matsuzaki, D. Pierson, M. Pulkkanen, O. O. Rusanovskaya, S. V. Shimaraeva, E. A. Silow, M. Schmid, M. A. Timofeyev, and P. Verburg

In 2024, the global average lake surface water temperature (LSWT) anomaly derived from satellite data during the warm season was +0.52°C with respect to the 1995–2020 baseline; the anomalies were positive for 79% and negative for 21% of the 1944 studied lakes. The 2024 anomaly is the largest since the record began in 1995. The mean LSWT trend during 1995 to 2024 was 0.22±0.01°C decade⁻¹, broadly consistent with previous analyses even though the number of lakes analyzed has doubled since 2022 (Woolway et al. 2017, 2018; Carrea et al. 2019, 2020, 2021, 2022a, 2023b, 2024; Fig. 2.2). The warm-season lake-mean LSWT anomalies for each lake are shown in Plate 2.1b.

In 2024, 56% of all observed lakes showed LSWT anomalies in excess of +0.5°C, and extensive regions with consistently large LSWT anomalies were detected. (Plate 2.1b). The largest positive anomalies were reported for lakes situated in Canada, China, Japan, the Tibetan area, eastern Europe, and the Middle East, while in Patagonia, Greenland, Alaska, and northeast Russia, lakes were found to be cooler than average.

<figure><img src="image_44.png" /><figcaption>Fig. 2.2. Annual time series of satellite-derived warm-season lake surface water temperature anomalies (°C; 1995–2020 base period) from 1995 to 2024 for lakes distributed (a) globally, and regionally in (b) Europe, (c) Africa, (d) Canada, and (e) the Tibetan Plateau.</figcaption></figure>
As in previous reports (see for example Carrea et al. 2023b, 2024), four regions were studied in more detail: Europe (number of lakes, n = 267, Figs. 2.2b, 2.3a), Africa (n = 146, Figs. 2.2c, 2.3b), Tibet (n = 145, Figs. 2.2e, 2.3d), and Canada (n = 495, Figs. 2.2d, 2.3c). In these areas, the warm-season LSWT anomalies generally align with the air temperature anomalies extracted at the locations of the lakes from the dataset compiled by NASA's Goddard Institute for Space Studies (GISS; Hansen et al. 2010; GISTEMP Team 2025). The average LSWT trend was +0.34±0.03°C decade⁻¹ in Europe (Fig. 2.2b) and +0.22±0.03°C decade⁻¹ in Canada (Fig. 2.2d). In Canada, 96% of observed lakes were warmer than average, with only 4% being cooler than average, and the mean LSWT anomaly was +0.87°C in 2024. In Europe, the average anomaly was +0.77°C, and 86% of lakes presented positive anomalies. In Africa and Tibet, the long-term change in LSWT is comparatively smaller, at +0.11±0.01°C decade⁻¹ and +0.17±0.02°C decade⁻¹, respectively (Figs. 2.2c,e). In Africa, 77% of the 146 lakes had positive LSWT anomalies, and the average anomaly in 2024 was +0.37°C. In Tibet, the average anomaly was +0.79°C, and the LSWT anomaly was positive for 143 lakes and negative for 2. In all these regions, the 2024 mean anomaly was the largest since the record began in 1995.

In situ single-point observations from 38 lakes were used to compute the warm-season temperature anomalies depicted in Fig. 2.4. Among these lakes, 27 have measurements for 2024, with an average anomaly of +1.25°C. Only three lakes experienced negative anomalies (average -0.59°C) and 24 lakes had positive anomalies (average +1.48°C) in 2024; Fig. 2.4 clearly shows that lakes are warming, especially after the year 2000. It is important to note that anomalies based on in situ measurements, which are point measurements, generally differ from those derived from satellite data, which instead represent lake-wide averages, and therefore are more representative of the lake response than single point. On the other hand, in situ measurements can offer high coverage in time while satellite data are sparser in time.

The period 1995–2020 is used as a baseline to compute the anomalies for both in situ (unless data were not available for the full period) and satellite temperature. The warm-season averages for midlatitude lakes were calculated

<figure><img src="image_45.png" /><figcaption>Fig. 2.3. Lake temperature anomalies (°C, colored dots) and 2-m air temperature anomalies (°C; NASA Goddard Institute for Space Studies [GISS]; Hansen et al. 2010, GISS Surface Temperature Analysis [GISTEMP] Team 2025) in 2024 for lakes in (a) Europe, (b) Africa, (c) Canada, and (d) the Tibetan Plateau. These values were calculated for the warm season (Jul–Sep in the extratropical Northern Hemisphere; Jan–Mar in the extratropical Southern Hemisphere; Jan–Dec in the tropics) with reference to the 1995–2020 base period.</figcaption></figure>

<figure><img src="image_46.png" /><figcaption>Fig. 2.4. In situ lake surface water temperature (LSWT) observations from 38 globally distributed lakes, showing the annually averaged warm season (Jul–Sep in the Northern Hemisphere; Jan–Mar in the Southern Hemisphere) anomalies (°C; 1995–2020 base period).</figcaption></figure>
for the summer months (July–September in the Northern Hemisphere and January–March in the Southern Hemisphere), while annual averages are used for tropical lakes located within 23.5° of the equator. For global averages, an unweighted mean of all the lake LSWT anomalies was computed. LSWT time series were derived from the Copernicus Climate Change Service (until 2022) and the Earth Observation Climate Information Service climate data record (Carrea et al. 2022b, 2023a), which was based on the European Space Agency Climate Change Initiative LAKES v2.1 dataset and subsequently extended. For 2024, LSWT was retrieved from satellite observations from Sea and Land Surface Temperature Radiometers (SLSTR) onboard Sentinel3A and 3B. The retrieval method of MacCallum and Merchant (2012) was applied on image pixels filled with water according to both the inland water dataset of Carrea et al. (2015) and a reflectance-based water detection scheme (Carrea et al. 2023a).

The satellite-derived LSWT data were validated with in situ measurements with an average satellite minus in situ temperature difference of less than 0.5°C (Carrea et al. 2023a). The satellite-derived LSWT data were averaged spatially for each of a total of 1944 lakes, and lake-wide average surface temperatures have been shown to give a more representative picture of LSWT responses to climate change than single-point measurements (Woolway and Merchant 2018).

The averaged surface air temperature was calculated from the Global Historical Climatology Network version 4 (GHCNv4; 250-km smoothing radius) data of the NASA GISS surface temperature analysis (Hansen et al. 2010; GISTEMP Team 2025).

### 3. NIGHT MARINE AIR TEMPERATURE

—R. C. Cornes, R. Junod, and E. C. Kent

The global annual average night marine air temperature (NMAT) for 2024 was 0.44°C above the 1991–2020 baseline in the University of Alabama in Huntsville Night Marine Air Temperature (UAHNMAT; Junod and Christy 2020) dataset and 0.54°C in the Climate Linked Atlantic Sector Science Night Marine Air Temperature (CLASSnmat; Cornes et al. 2020) dataset. These values represent the highest in the record dating to 1900 and are 0.04°C higher than those of 2023 in both datasets.

Between 1900 and 2024, annual global average NMAT increased at a rate of 0.07°C decade⁻¹ in UAHNMAT and 0.08°C decade⁻¹ in CLASSnmat. As noted in previous *State of the Climate* reports (e.g., Cornes et al. 2023), sea surface temperatures (SSTs) have been increasing faster than NMAT for reasons that are not fully understood. For example, over the same 1900–2024 period, global annual averages in the Hadley Centre Sea Surface Temperature Dataset version 4 (HadSST4; Kennedy et al. 2019) increased at a rate of 0.09°C decade⁻¹. As a result, SST anomalies have consistently been higher than NMAT anomalies over the past decade (Fig. 2.5). The differences between SST and NMAT for individual years over that period have generally not been statistically significant due to the size

<figure><img src="image_47.png" /></figure>

**Fig. 2.5.** Annual average night marine air temperature anomalies (°C; 1991–2020 base period) calculated from the Climate Linked Atlantic Sector Science Night Marine Air Temperature (CLASSnmat), University of Alabama in Huntsville Night Marine Air Temperature (UAHNMAT), and Hadley Centre Sea Surface Temperature Dataset version 4 (HadSST4) datasets averaged over the (a) globe, (b) northern extratropics, (c) tropics, and (d) southern extratropics. The tropics is defined as the latitude range 30°S–30°N and the northern (southern) extratropics as >30°N (<30°S). The averages only include values that are common to all three datasets for a given year; since UAHNMAT starts in 1900, only values for the period 1900–2024 are plotted.
of uncertainty estimates in the datasets (with 2-σ uncertainty around 0.1°C), although the large
differences seen in 2021/22 have not been observed over the last two years.

Record temperatures were observed across the tropics in 2024, with anomalies of +0.54°C in CLASSnmat and +0.44°C in UAHNMAT. In the northern extratropics, however, NMAT values in 2024 were slightly lower than in 2023 (c.f. +0.71 in 2023 and +0.63°C in 2024 in CLASSnmat and +0.55 versus +0.53°C in UAHNMAT), making 2024 the second-warmest year in the record. In contrast, HadSST4 recorded 2024 as the warmest year in the northern extratropics, with an anomaly of +0.84°C. To prevent unequal spatial sampling affecting these results, all three datasets have been masked to ensure common coverage.

Regional differences in NMAT anomalies are further illustrated in Plate 2.1c. Temperature anomalies above +0.5°C were widespread across the globe in 2024, with especially large anomalies of up to +1.5°C relative to the 1991–2020 baseline observed across certain areas and notably across the northwestern Pacific. Negative temperature anomalies were present in the southeastern Pacific—and to a lesser extent, the northeastern Pacific—linked to the weakening El Niño conditions throughout 2024 and the switch to La Nina-like conditions at the end of the year (see section 4b for details). This change is reflected in the global monthly averages for NMAT and SST (Fig. 2.6). Starting in June 2023, exceptional temperature anomalies were recorded in these data (Cornes and Junod 2024) and continued into early 2024, with the highest anomalies peaking in January. While temperature anomalies slightly reduced in the second half of 2024 compared to 2023, they remained notably higher than previous years, with anomalies still 0.4°C above 1991–2020 levels.

The causes of these anomalously high
temperatures in 2023 and 2024 have been
variously discussed in the literature (see also
section 2b1). Cattiaux et al. (2024) suggest that
a combination of long-term anthropogenic
warming and a significant peak in internal
variability accounts for the observed condi-
tions. Gettelman et al. (2024) propose that the
anomalies in 2022/23 are due to an increase
in net radiative forcing, driven by mandated
reductions in ship-based sulfur emissions
that came into effect in 2020. Further analysis
is needed to understand the relative contri-
butions of these factors to NMAT versus SST.
However, based on the results presented here,
no distinct difference is observed between
NMAT and SST in terms of large-scale averages
over the past two years. The fact that global and
tropical average anomalies from CLASSnmat

are now comparable to those from HadSST4 for the first time in 10 years may suggest an external forcing mechanism. However, the differences between UAHNMAT and CLASSNmat—despite using essentially the same input data, but undergoing different quality control and bias adjustment processes—are of the same magnitude as the differences between SST and NMAT. This suggests that structural uncertainties in dataset preparation may obscure any potential external forcing mechanisms.

4. SURFACE TEMPERATURE EXTREMES

—R. J. H. Dunn, M. G. Donat, S. Kirkpatrick, and M. G. Bosilovich

Ongoing record-breaking average global surface temperatures in 2024 (section 2b1) again was accompanied by further record-high numbers of warm days (TX90p; Table 2.2) and record-low numbers of cool nights (TN10p; Table 2.2) across all datasets assessed herein.

The globally averaged number of warm days indicated by the Global Historical Climatology Network Daily Extremes (GHCNDEX) dataset of gridded in situ observations (Donat et al. 2013) was 75±7 days, four days more than the value for 2023 (Fig. 2.7a; Table 2.2). At the other end of

<figure><img src="image_48.png" /><figcaption>Fig. 2.6. Global monthly average night marine air temperature anomalies (°C; 1991–2020 base period) in the (a) Climate Linked Atlantic Sector Science Night Marine Air Temperature (CLASSnmat), (b) University of Alabama in Huntsville Night Marine Air Temperature (UAHNMAT), and (c) Hadley Centre Sea Surface Temperature Dataset version 4 (HadSST4) datasets. Each line represents a year of data, and the results for 2023 and 2024 are shown in blue and red, respectively.</figcaption></figure>
the scale, there were only 13±8 cool nights, the lowest in this dataset (starting 1951; Fig. 2.7b) and substantially less than half the expected number (36.5 days by definition). As outlined in previous reports (e.g., Dunn et al. 2024a), GHCNDEX has severely limited spatial coverage for recent years (Appendix Fig. A2.5); thus three reanalysis products (ERA5, Hersbach et al. 2020, Bell et al. 2021, Soci et al. 2024; JRA-3Q, Kosada et al. 2024; MERRA-2, Gelaro et al. 2017) were used to give a globally complete (including Antarctica) assessment of the land surface extreme temperatures following Dunn et al. (2022b). As shown in Fig. 2.7c and Table 2.2, all these products show record-high and record-low values in 2024 for the number of warm days and cool nights, respectively, in the global average over land. The large differences between the GHCNDEX values in Table 2.2 and those from ERA5 and JRA-3Q using a 1961–90 reference period are likely due to the low spatial coverage of GHCNDEX (Appendix Fig. A2.5).

Especially high numbers of warm days were experienced across almost all of Africa and Central and South America as well as in northeastern Canada, eastern and southern Europe, and parts of the Middle East, China, and Southeast Asia. Some of these areas experienced twice as many warm days as would be expected on average within a year during the baseline period (Plate 2.1d). During 2024, many of these areas had the regionally highest number of warm days on record (Fig. 2.8a), often associated also with the highest absolute temperatures, particularly in parts of Central and South America and Africa (Fig. 2.8e). Moreover, areas in South America broke records in numbers of warm days (TX90p), which were only set last year (Fig. 2.8 in Dunn et al. 2024a).

Figure 2.8b shows the time series from calculating the land fraction setting record-high numbers of warm days sequentially in each year, i.e., the land area fraction in Fig. 2.8a categorized as “highest” but working through each year in turn to determine the area setting new records of TX90p per year. The first year will by default set a record value across the entire globe, but in a stable climate, records should become rarer over time. Therefore, the values from the first 20 years of each reanalysis are not shown. JRA-3Q has the largest land area experiencing record numbers of warm days in 2024, but a lower fraction than ERA5 which, along with MERRA-2, has the second largest (ERA5 has most in 2010 and MERRA-2 in 2002). For TN10p, in 2024 MERRA-2 has the largest area with new record-fewest numbers of cool nights, with JRA-3Q and ERA5 tied for second in 1998. The fraction of land with the highest annual maximum temperature (TXx; Fig. 2.8f; Table 2.2) is greatest in MERRA-2 and a close second to 1983 in JRA-3Q.

<figure><img src="image_49.png" /><figcaption>Fig. 2.7. (a),(b) Time series of the (a) annual number of warm days (TX90p) and (b) cool nights (TN10p) averaged over global land regions based on gridded station data from Global Historical Climatology Network Daily Extremes dataset (GHCNDEX), ERA5, and the Japanese Reanalysis for Three Quarters of a Century (JRA-3Q) reanalyses using 1961–90 as the reference period. The spatial coverage in GHCNDEX is limited; the black dashed lines show the percentage of land area covered (right y-axis). The 2-σ coverage uncertainty (following Brohan et al. 2006; Dunn et al. 2020) is shown by the light red bands in (a),(b), (c), (d) As in (a),(b), for three atmospheric reanalyses (ERA5, MERRA-2, and JRA-3Q) using 1991–2020 as the reference period.</figcaption></figure>
Lower-than-expected numbers (36.5) of cool nights (TN10p) were found almost globally, with only parts of the Amazon basin and central-southern Africa experiencing higher-than-expected numbers (Plate 2.1e). This index is zero bounded (there cannot be fewer than zero cool nights, an anomaly of -36.5 days), in contrast to TX90p, which is bounded to zero for cold anomalies but asymmetrically bounded to 100% of days (i.e., 365 per year) for warm anomalies; with our choice of reference period in the current climate, we are far from this upper bound. Therefore, the magnitude of the anomalies in TN10p appear smaller as this index asymptotically reduces to zero as the temperature distribution moves farther from the 10% threshold (Dunn and Morice 2022).

Regionally, the record-lowest numbers of cool nights (TN10p) were found across much of tropical and northern Africa, Southeast Asia, eastern Europe, and northern South America (Fig. 2.8c), and the global area with record-low values is largest (in MERRA-2) or second largest (in ERA5 and JRA-3Q, both record in 1998) within the periods of the respective datasets (Fig. 2.8d).

<figure><img src="image_50.png" /><figcaption>Fig. 2.8. Maps indicating grid cells where (a) the warm day index (TX90p) from ERA5 (since 1940), (c) the cool night index (TN10p) from the Japanese Reanalysis for Three Quarters of a Century (JRA-3Q; since 1948), and (e) the annual maximum temperature (TXx) from MERRA-2 (since 1980) for 2024 ranked in the three highest (orange to red) or three lowest (blue) values. Time series of the percent of land area ranked as the highest value for (b) TX90p, (d) TN10p, and (f) TXx sequentially in each year for ERA5 (from 1960), JRA-3Q (from 1968), and MERRA-2 (from 2000). The ranks from the first 20 years of each reanalysis are not calculated.</figcaption></figure>
A number of indices have been recommended by the World Meteorological Organization to characterize temperature and precipitation extremes (Zhang et al. 2011; Dunn et al. 2024b), of which we present three here (Table 2.2). Dunn et al. (2022b) show there is good agreement between the observation-based and reanalysis products, especially for the two percentile-based indices used in this section. See Donat et al. (2013) and Dunn et al. (2022b) for details of indices from the observation-based and reanalyses datasets, respectively.

The indices shown here use fixed reference periods, and an intercomparison between these is not trivial (Dunn et al. 2020; Yosef et al. 2021; Dunn and Morice 2022); we show versions using both 1961–90 (for GHCNDEX, ERA5, and JRA-3Q) and 1991–2020 (for ERA5, MERRA-2, and JRA-3Q).

<table><caption>Table 2.2. Definitions of indices used for land surface temperature extremes, their globally averaged values (days) for 2024, and ranks from the four datasets. Coverage uncertainties are shown for GHCNDEX.</caption><thead><tr><th>Index</th><th>Name</th><th>Definition</th><th>GHCNDEX<br>(1951–2024)<br>Value, [rank]<br>Ref. Period<br>1961–90</th><th>ERA5<br>(1940–2024)<br>Value, [rank]<br>Ref. Period<br>1961–90</th><th>JRA-3Q<br>(1948–2024)<br>Value, [rank]<br>Ref. Period<br>1961–90</th><th>ERA5<br>(1940–2024)<br>Value, [rank]<br>Ref. Period<br>1991–2020</th><th>JRA-3Q<br>(1948–2024)<br>Value, [rank]<br>Ref. Period<br>1991–2020</th><th>MERRA-2<br>(1980–2024)<br>Value, [rank]<br>Ref. Period<br>1991–2020</th></tr></thead><tbody><tr><td>TX90p</td><td>Warm days</td><td>Annual count of days when the daily maximum temperature exceeds the 90th percentile</td><td>75±7<br>[highest]</td><td>107<br>[highest]</td><td>94<br>[highest]</td><td>73<br>[highest]</td><td>68<br>[highest]</td><td>72<br>[highest]</td></tr><tr><td>TN10p</td><td>Cool nights</td><td>Annual count of nights when the daily minimum temperature falls below the 10th percentile</td><td>13±8<br>[lowest]</td><td>13<br>[lowest]</td><td>12<br>[lowest]</td><td>20<br>[lowest]</td><td>18<br>[lowest]</td><td>20<br>[lowest]</td></tr><tr><td>TXx</td><td>Warmest T-max</td><td>Annual maximum of the maximum temperature</td><td>36.5±0.3<br>[highest]</td><td>-</td><td>-</td><td>31.1<br>[highest]</td><td>30.6<br>[highest]</td><td>31.7<br>[highest]</td></tr></tbody></table>
## Sidebar 2.1: Super extreme land surface temperature hotspots

—E. GOOD, J. BLANNIN, A. WARING, K. VEAL, AND D. GHENT

It is well documented that as Earth's climate warms, the frequency of extreme heat events is increasing (IPCC 2021; Dunn 2024a; Willett 2023b). Evidence suggests that the fraction of land becoming uninhabitable due to extreme heat will also increase with global warming (Matthews et al. 2025). Surface temperatures are traditionally monitored using data from weather stations measuring near-surface air temperature at ~1.5m-2m above the surface (T2m). However, station density and location limit spatial coverage and, therefore, knowledge of how extreme temperature frequency and intensity are evolving in many regions of the world (section 2b4; Dunn et al. 2024).

An alternative, independent source of information can be obtained from satellite observations of land surface temperature (LST) derived from sensors operating in the infrared (IR)

and microwave regions of the electromagnetic spectrum. An advantage of using LST from satellites over ground-based T2m is in the global provision of data. However, a disadvantage is that the LST cannot be measured directly, as it relies on modeling of the impact of the atmosphere between the satellite and the surface to estimate it; furthermore, in the case of IR LSTs, only cloud-free observations are available. Additionally, although strongly correlated, LST and T2m are different variables. Over bare surfaces, LST represents how hot Earth's surface is to the touch, whereas over dense vegetation, it more closely represents the canopy surface temperature. Therefore, simultaneous LST-T2m differences often reach several °C and may exceed 20°C in some conditions (Good 2016). However, studies have shown that the long-term signal of change observed in LST and T2m datasets is similar

<figure><img src="image_51.png" /></figure>

Fig. SB2.1. Time series for spatially matched 5° latitude-longitude data between ±60° latitude for (a) Sea and Land Surface Temperature Radiometer onboard the Sentinel-3A platform (SLSTR/A) land surface temperature (LST)-Climatic Research Unit temperature version 5 (CRUTEM5) anomalies of near-surface air temperature at ~1.5m-2m above the surface (T2m; reference baseline period is May 2016 to Dec 2024; the CRUTEM5 anomalies have been adjusted from the 1961-90 baseline period following Good et al. [2017]) and (b) Sea and Land Surface Temperature Radiometer onboard the Sentinel-3B platform (SLSTR/B) LST-CRUTEM5 T2m anomalies (baseline period Nov 2018 to Dec 2024). Trends have been calculated using the Theil-Sen/median of pairwise slopes method (Sen 1968). The Mar 2019 outlier in the SLSTR/B time series is due to 19 days of missing SLSTR/B data. The Nov 2018 outlier is outside the super extreme hotspot analysis period. Example 5° latitude-longitude anomaly maps for April 2024 for (c) SLSTR/B LST and (d) CRUTEM5 are also shown. All units are °C.
(Good et al. 2017, 2022). The objective of this analysis is to
demonstrate how LST may be used to map and monitor super
extreme hotspots (SEHs)—where Earth may already be, or is
becoming, uninhabitable under climate change. Using LST is
advantageous as many of these SEHs occur in regions with few
T2m observations.

The data used are from the Sea and Land Surface Temperature Radiometer onboard the Sentinel-3B platform (SLSTR/B), which has been in polar orbit since April 2018. LST data for November 2018 to December 2024 have been obtained from the European Space Agency (ESA) Climate Change Initiative for LST (LSTCCI; https://climate.esa.int/en/projects/land-surface-temperature/; version 4.00) and, for recent months in 2024, from the U.K. Earth Observation Climate Information Service (https://eocis.org/). The data from both sources are consistent and have been processed using the same approach (Ghent et al. 2024). The SLSTR/B instrument images Earth at ~1-km spatial resolution, providing a near-global view each day. However, as the SLSTR/B operates in the IR, only cloud-free LSTs are available. This dataset was selected as it is the only climate-quality LST dataset currently available for 2024. The widely-used LSTs from the Moderate Resolution Imaging Spectroradiometer (MODIS) sensors (2000-present) cannot be used, as these data are temporally unstable from around 2021 due to a changing observation time (https://modis-land.gsfc.nasa.gov/news.html). Similarly, data from the SLSTR onboard Sentinel-3A (SLSTR/A: 2016-24) cannot be used, as this dataset is unstable due to a variation in the processing by the ESA of the raw satellite data time series. Establishing temporal stability of LST datasets is crucial due to its often strong diurnal variation (Good 2016); a changing overpass time or any other non-climatic discontinuity in the dataset can have a critical effect on time series analysis.

Following the approach used by Good et al. (2017, 2022),
the stability of SLSTR/A and /B between ±60° latitude is
assessed using Climatic Research Unit temperature version
5 (CRUTEM5; Osborn et al. 2021). CRUTEM5 is a monthly 5°
latitude-longitude T2m dataset based on homogenized
global station data and provides a stable reference for
assessing LST stability. There is a statistically significant
slope in the mean daily SLSTR/A-minus-CRUTEM5 anomaly
time series (Fig. SB2.1a, 0.42°C decade⁻¹; 95% confidence
interval [CI] 0.31°C decade⁻¹ to 0.54°C decade⁻¹) as well as a
non-climatic discontinuity. By contrast, the SLSTR/B dataset
(Fig. SB2.1b) appears stable (−0.08°C decade⁻¹; 95% CI −0.24°C
decade⁻¹ to 0.09°C decade⁻¹) and the monthly SLSTR/B and
CRUTEM5 anomalies are similar and well correlated (Pearson
correlation coefficient r = 0.81). Also shown in Figs. SB2.1c,d
are examples of the SLSTR/B and CRUTEM5 anomaly maps for
April 2024. The spatial pattern of the anomalies shows a high
degree of spatial consistency (r = 0.84), confirming the overall
similarity between the clear-sky LST and all-sky T2m signals
and the more complete spatial coverage of the LST data.

With the stability of the SLSTR/B LSTs assured, the occur-
rence and temporal evolution of SEHs can be characterized. For
context, Fig. SB2.2 shows the maximum SLSTR/B LST observed
during 2024. The hottest regions occur in western North
America, North Africa, the Arabian Peninsula, parts of South
and Central Asia, and Australia, where LSTs of >60°C can occur.
Using only full-year daytime data between 2019 and 2024,
thresholds of 50°C and 55°C are used to identify SEHs; for
context, 55°C is above the 99th percentile for the
2019-24 SLSTR-B data (53.2°C), thus these are globally
extreme LSTs. Fig. SB2.3a shows the locations of SEHs in 2024;
together with exceedances of other LST thresholds, SEHs are
prevalent in the Arabian peninsula, Iran (Lut desert), across
central Asia, parts of North Africa, western North America, and
Australia, which is generally consistent with Mildrexler et al.
(2006) and Zhao et al. (2021), who used MODIS to map global
LST hotspots. Figure SB2.3b shows the annual time series of
the number of SEH locations. Figure SB2.3c shows the fraction
of valid observations across all grid cells exceeding each
threshold, essentially representing the accumulated global
frequency of SEHs each year. Both time series have small
negative trends, although the p values indicate these are sta-
tistically insignificant. This is not surprising given the relatively
short six-year period of data compared to the 30+ year record
often used for climate applications. For all SEH metrics, 2019 is
ranked as the most extreme year, followed by 2023, while

<figure><img src="image_52.png" /><figcaption>Fig. SB2.2. Maximum land surface temperature (LST; °C) observed during 2024 by the Sea and Land Surface Temperature Radiometer onboard the Sentinel-3B platform (SLSTR/B) at 0.1° latitude–longitude. The percentiles from the 2024 distribution of maximum LSTs at the native 0.01° spatial resolution are: 50.5°C (95th), 57.7°C (99th), and 62.3°C (99.9th). The hottest LSTs above ~62°C often occur in isolated grid cells and are associated with lava flows or wildfires.</figcaption></figure>
2024 is third for both 50°C metrics and fourth for both 55°C metrics (i.e., both for the number of locations and total fraction per year). With a longer stable LST time series, this analysis

demonstrates how LST could be used to monitor global heat extremes and the occurrence of SEHs, which may not be observable using T2m data.

<figure><img src="image_53.png" /></figure>

Fig. SB2.3. (a) Map showing where two or more valid Sea and Land Surface Temperature Radiometer onboard the Sentinel-3B platform (SLSTR/B) land surface temperature (LST) observations in 2024 exceed selected LST thresholds (25°C, 30°C, 35°C, 40°C, 45°C, 50°C, and 55°C); the SLSTR/B data have been resampled to 0.1° latitude-longitude. The time series of the number of (b) 0.1° grid cells for each year that exceed the 50°C (black) and 55°C (red) thresholds and (c) sum of the annual fraction of valid observations in each year that exceed these thresholds across all grid cells. The linear trends and p values are shown for information.
# 5. TROPOSPHERIC TEMPERATURE

—S. Po-Chedley, J. R. Christy, C.-Z. Zou, C. Mears, and L. Haimberger

Record-high global lower tropospheric temperature (LTT) values in the second half of 2023 persisted through boreal summer in 2024 (Fig. 2.9), producing a 2024 global-mean LTT that was 0.84°C (0.77°C–0.92°C depending on dataset) above the 1991–2020 climatological mean (Fig. 2.10). All nine tropospheric temperature datasets considered here (Table 2.3) ranked 2024 as the warmest year for global LTT, making 2023 the second-warmest year in most datasets.

Some of the recent tropospheric warmth is attributable to the strong El Niño event that peaked in the boreal winter of 2023/24. During El Niño events, tropical and global tropospheric temperatures lag behind warm central and eastern Pacific sea surface temperatures by three to five months, which helps to explain the exceptional warmth in the first half of 2024 (Fig. 2.10). Although the ENSO relaxed to neutral conditions by April–June and approached La Niña conditions by the end of the year (see section 4b for details), global LTT remained close to the record-breaking warmth in the final months of 2024 (Fig. 2.9). Most of the tropics and roughly half of Earth overall experienced record-warm LTT conditions in 2024 (Plate 2.1f).

Although the warmth seen over 2023/24 was above normal, it is consistent with long-term, greenhouse gas-driven warming that is evident in both global LTT and tropical tropospheric temperatures (TTTs) across a variety of tropospheric temperature datasets (Table 2.3); this is the fourth time in the last 10 years that record-warm global tropospheric temperatures have been reported in the *State of the Climate* (Po-Chedley et al. 2024, 2021; Christy 2017). Tropospheric temperature time series are derived from sparse weather balloon-based radiosonde records that measure temperatures as a function of height, satellite-borne microwave measurements that

provide near-global observations over broad atmospheric layers, and reanalysis models that ingest and combine many observations to produce horizontally and vertically resolved estimates and forecasts of numerous

<figure><img src="image_54.png" /></figure>

**Fig. 2.9.** (a) Global and (b) tropical (20°S–20°N) lower-tropospheric temperature (LTT) pentad averages over two-year segments (gray lines) from the University of Alabama in Huntsville (UAH) dataset. Each year is shown only once: odd (even) years are on the left (right) side of the figure. The most recent segment (2023/24) is shown in black and the hottest and coldest pentad values (of all years) are shown in red and blue, respectively.

<figure><img src="image_55.png" /></figure>

**Fig. 2.10.** Monthly average global lower-tropospheric temperature (LTT) anomalies for (a) satellite, (b) radiosonde, and (c) reanalysis datasets. In panel (d), red (blue) denotes positive (negative) values in the Niño-3.4 index. Annual averages are displayed for the Radiosonde Atmospheric Temperature Products for Assessing Climate (RATPAC)-A dataset. Anomalies are with respect to a 1991–2020 base period.
geophysical variables, including atmospheric temperature. In this report, the vertically resolved atmospheric temperature data from radiosondes and reanalyses are converted into a satellite-equivalent LTT time series, which represents the average temperature over a layer from the surface to ~10 km, and a TTT time series, which spans the surface to the tropical tropopause. Despite diversity in measurement methods and calibration procedures, all of the datasets exhibit similar variability and long-term trends (Fig. 2.10; Table 2.3).

The annual-mean tropospheric temperature was above the 1991–2020 climatological mean over more than 90% of Earth’s surface (Plate 2.1f). Record-setting values covered about half of Earth’s surface, including the deep tropics (20°S–20°N), Europe, eastern Canada, East Asia, and parts of the Southern Ocean. Below-average tropospheric temperatures were observed over less than 5% of Earth’s surface (Plate 2.1f).

The recent surge in global temperatures in the past two years deserves continued scrutiny. While the contributions of internal variability and greenhouse gas-driven warming are key components of the record LTT values over 2023 and 2024, other factors including the solar cycle, reductions in ship-based aerosol emissions, and multi-year trends in planetary albedo have also likely played a role (e.g., Raghuraman et al. 2024; Goessling et al. 2025; Gettelman et al. 2024). The evolution of the global climate beyond 2024 will be of significant scientific interest as we strive to better understand the factors that contributed to the recent exceptional tropospheric warmth.

<table><caption>Table 2.3. Temperature trends (°C decade⁻¹) for global lower-tropospheric temperature (LTT) and tropical (20°S–20°N) tropospheric temperature (TTT) over the periods 1958–2024 and 1979–2024. NASA MERRA-2 data begin in 1980 and NOAA v5.0 LTT begins in 1981. Cells marked with a dash signify that the data do not extend back to 1958.</caption><thead><tr><th>Method</th><th>Dataset</th><th>LTT (90°S–90°N)<br>1958</th><th>LTT (90°S–90°N)<br>1979</th><th>TTT (20°S–20°N)<br>1958</th><th>TTT (20°S–20°N)<br>1979</th></tr></thead><tbody><tr><td>Radiosonde</td><td>NOAA RATPAC vA2<br>(Free et al. 2005)</td><td>0.19</td><td>0.24</td><td>0.18</td><td>0.20</td></tr><tr><td>Radiosonde</td><td>RAOBCORE v1.9<br>(Haimberger et al. 2012)</td><td>0.17</td><td>0.19</td><td>0.15</td><td>0.17</td></tr><tr><td>Radiosonde</td><td>RICH v1.9<br>(Haimberger et al. 2012)</td><td>0.19</td><td>0.21</td><td>0.19</td><td>0.21</td></tr><tr><td>Satellite</td><td>UAH v6.1<br>(Spencer et al. 2017)</td><td>-</td><td>0.15<sup>[1]</sup></td><td>-</td><td>0.15</td></tr><tr><td>Satellite</td><td>RSS v4.0<br>(Mears and Wentz, 2016)</td><td>-</td><td>0.23</td><td>-</td><td>0.19</td></tr><tr><td>Satellite</td><td>NOAA v5.0<br>(Zou et al. 2023)</td><td>-</td><td>0.15<sup>[1]</sup></td><td>-</td><td>0.13</td></tr><tr><td>Reanalysis</td><td>ERA5<br>(Hersbach et al. 2020)</td><td>0.17</td><td>0.20</td><td>0.17</td><td>0.19</td></tr><tr><td>Reanalysis</td><td>JRA-3Q<br>(Kosaka et al. 2024)</td><td>0.19</td><td>0.20</td><td>0.19</td><td>0.18</td></tr><tr><td>Reanalysis</td><td>NASA MERRA-2<br>(Gelaro et al. 2017)</td><td>-</td><td>0.21</td><td>-</td><td>0.21</td></tr><tr><td>Median</td><td>N/A</td><td>0.19</td><td>0.20</td><td>0.18</td><td>0.19</td></tr></tbody></table>

<footnote>[1] The vertical sampling in UAH and NOAA LTT is slightly different from other datasets and results in temperature trends that are approximately 0.01°C decade⁻¹ smaller than in other datasets.</footnote>
# 6. STRATOSPHERIC TEMPERATURE

—W. J. Randel, C. Covey, L. Polvani, and A. K. Steiner

Global-mean temperatures in the lower, middle, and upper stratosphere increased slightly during 2024, mainly reflecting a recovery from anomalous cooling due to the Hunga volcanic eruption in early 2022. The long-term trends during the satellite era of 1979–2024, however, show multi-decadal cooling of the stratosphere due to ozone depletion in the first two decades as well as anthropogenic CO₂ increases over the whole period. The Arctic stratospheric polar vortex was disturbed by two major stratospheric warming events during early 2024, while the Antarctic polar vortex was strong and persistent during the year. The stratospheric quasi-biennial oscillation (QBO) progressed normally in 2024, with equatorial westerly zonal wind shears and positive temperature anomalies descending from the middle to lower stratosphere during the year.

Global stratospheric temperatures have been monitored from satellite observations for over 40 years, and Fig. 2.11a shows time series of global monthly temperature anomalies spanning the lower to upper stratosphere and mesosphere from merged satellite data. In addition to long-term stratospheric cooling (due to CO₂ increases and stratospheric ozone changes), transient variations arise from the 11-year solar cycle and effects of large volcanic eruptions: El Chichón (1982), Pinatubo (1991), and Hunga (2022). Transient warming of the lower stratosphere is also evident following the Australian wildfires in early 2020 (Yu et al. 2021; Stocker et al. 2021). Effects of the volcanic eruptions and wildfires are more easily seen in Fig. 2.11b, which shows global temperature anomalies after removing the decadal-scale trends and solar cycle effects. The time series show anomalous cooling of the middle to upper stratosphere by 0.5°C–1.0°C in 2022/23, caused by radiative impacts of the unusually large quantities of water vapor (H₂O) injected directly to the stratosphere by the January 2022 Hunga volcanic eruption (Millan et al. 2022; Stocker et al. 2024; Randel et al. 2024).

Fig. 2.11. (a) Time series of monthly global average temperature anomalies (°C) for thick-layer averages spanning the lower to upper stratosphere and mesosphere (bottom to top). Middle and upper stratosphere data are from the Stratospheric Sounding Unit (SSU) updated with Microwave Limb Sounder (MLS) measurements, representing thick-layer averages centered near 30 km, 38 km, and 45 km (SSU1, SSU2 and SSU3, respectively). Lower-stratosphere temperatures (TLS) are ~13 km–22 km layer averages from satellite microwave measurements. These datasets are discussed in Steiner et al (2020). Mesosphere temperatures are 50-km–70-km averages from MLS data. (b) Global average temperature anomalies (°C) for each level, derived by subtracting linear trend and solar cycle fits to the time series in (a). These fits for each level are shown as dashed lines in panel (a). The vertical lines in (b) denote the El Chichón (1982), Pinatubo (1991), and Hunga (2022) volcanic eruptions. Figure updated from Randel et al (2024).

<figure><img src="image_56.png" /></figure>
The Hunga water vapor anomalies have been diffusing globally and decreasing over time (e.g., Zhou et al. 2024), resulting in smaller stratospheric radiative impacts and a partial recovery from the anomalous cooling during 2024. The 11-year solar cycle was also near a maximum during 2024 (https://www.swpc.noaa.gov/products/solar-cycle-progression), contributing to slightly higher temperatures in the middle and upper stratosphere.

We note that the observed Hunga cooling over 2022–24 in the middle stratosphere is comparable in magnitude, but opposite in sign, to warm anomalies tied to the El Chichón (1982) and Pinatubo (1991) volcanic eruptions. The difference in sign is due to the stratospheric cooling effects of large water vapor anomalies from Hunga, whereas the El Chichón and Pinatubo warmings arise from sulfate aerosol-dominated warming effects. While the magnitude of the cooling anomalies due to Hunga increases with altitude, the magnitude of warming anomalies due to El Chichón and Pinatubo decreases with altitude. In the mesosphere, the Hunga cooling is a result of H₂O-induced ozone depletion (Randel et al. 2024).

Variations in polar vortex temperatures contribute little to global temperature anomalies but provide context for regional variability. Time series of Arctic and Antarctic polar vortex temperatures in 2024 are shown in Fig. 2.12, highlighting strong variability in the Arctic, with two separate stratospheric warming events in early 2024 (Lee et al. 2025). These events were caused by enhanced planetary wave forcing from the troposphere; the occurrence of two major warming events in the same year is unusual but has been observed several times in the past and has little to do with long-term climate change. In contrast, the Antarctic polar vortex shows a larger annual cycle with relatively little variability, although a series of weak wave-induced warming events were observed during austral mid-winter 2024.

## 7. EQUIVALENT TEMPERATURE

—T. Matthews, T. Wood, P. Stoy, and M. Byrne

Global-mean equivalent temperature (Teq) reached its highest level in 2024 since at least 1979 according to reanalyses data, breaking its previous record (set in 2023) by 0.35°C–0.38°C.

Teq was introduced last year in the *State of the Climate in 2023* (Matthews et al. 2024). It is a more complete metric than air temperature (T) for tracking heat accumulation in the atmosphere:

$$Teq = T + qL/C_p$$

where T is the (dry-bulb) air temperature, q is the specific humidity (kg kg⁻¹), L is the latent heat of vaporization, and Cₚ is the specific heat capacity of the air. Note that Teq/Cₚ thus yields the ‘moist static energy’ that has also the subject of trend assessments (e.g., Peterson et al. 2011). Teq is therefore also closely related to wet-bulb temperature (Tw; section 2d2), but it has the advantage

<figure><img src="image_57.png" /><figcaption>Fig. 2.12. (a) Time series of Arctic polar cap (60°N–90°N) temperatures in the lower stratosphere (50 hPa) during 2024, compared with the historical distribution of temperatures since 1978/79. Green arrows denote two stratospheric major warming events in early 2024. (b) Similar time series over the Antarctic (60°S–90°S) at 50 hPa. Note the different vertical axes between (a) and (b), and that the time axes are shifted by six months. (Data are from https://acd-ext.gsfc.nasa.gov/Data_services/met/ann_data.html.)</figcaption></figure>
of being linearly related to atmospheric heat accumulation; by comparison successive 1°C increments in T<sub>w</sub> require progressively more heat accumulation.

The second term comprising *Teq* (the ‘latent temperature’, *Tq*) grows exponentially with *T* if relative humidity (*RH*) remains constant (due to the non-linearity in saturation vapor pressure as described by the Clausius Clapeyron equation). Hence, using *T* alone increasingly downplays the magnitude of changes in *Tq* and, accordingly, total heat content as the climate warms. This problem is not spatially uniform and is greater in regions with higher baseline *Tq* (such as within the tropics), where in some regions *Tq* is already rising faster than *T* (Matthews et al. 2022). Not only is *Teq* therefore more theoretically appropriate to track atmospheric heat accumulation as the climate changes, it is also more closely related to some key societal impacts. For example, extreme precipitation and human heat stress should be expected to scale more with *Teq* than *T* (Matthews et al. 2022; Song et al. 2022; Stoy et al. 2022). Note, however, that the closely related T<sub>w</sub> remains much more widely used in the study of heat extremes (section 2d2).

*Teq* anomalies for 2024 are assessed here using ERA5 (Hersbach et al. 2020) and JRA-3Q (Kosaka et al. 2024) reanalyses, along with the land-only station-based Met Office Hadley Centre Integrated Surface Dataset of Humidity over land (HadISDH.land; Willett et al. 2013, 2014). Globally (over land and ocean), *Teq* in 2024 was the highest on record according to both reanalyses datasets (Fig. 2.13), exceeding the previous records set in 2023 with departures of +1.57°C and +1.65°C above the 1991–2020 baseline for ERA5 and JRA-3Q, respectively (Table 2.4). Both datasets also rank the constituent parts of *Teq* (*T* and *Tq*) in the global series highest in 2024 (Appendix Tables A2.1–A2.3).

Across ERA5 and JRA-3Q, the mean 2024 global *Teq* anomaly (1.61±0.06°C, where uncertainty is one standard deviation across datasets) is much larger than that of *T* (0.70±0.04°C).

<figure><img src="image_58.png" /><figcaption>Fig. 2.13. (a),(d),(g) 2-m air temperature (T), (c),(f),(i) equivalent temperature (Teq), and (b),(e),(h) latent temperature (Tq) for (a),(b),(c) ocean + land, (d),(e),(f) land-only, and (g),(h),(i) ocean-only spatial mean anomalies versus the 1991–2020 baseline (°C). Each dataset is shown by colored lines: ERA5 (purple) and the Japanese Reanalysis for Three Quarters of a Century (JRA-3Q; cyan). Land-only sub-figures (d),(e),(f) also show Met Office Hadley Centre Integrated Surface Dataset of Humidity (HadISDH) anomalies (dark gray).</figcaption></figure>

<table><caption>Table 2.4. Global equivalent temperature (Teq) anomalies and their constituent parts (T, Tq) in °C (1991–2020 base period) for 2024 and [rank] since 1979 (the earliest year common to all datasets). The uncertainty in the ensemble mean column represents one standard deviation.</caption><thead><tr><th rowspan="2">Dataset</th><th>ERA5</th><th>ERA5</th><th>ERA5</th><th>JRA-3Q</th><th>JRA-3Q</th><th>JRA-3Q</th><th>HadISDH</th><th>HadISDH</th><th>HadISDH</th><th>Ensemble Mean</th><th>Ensemble Mean</th><th>Ensemble Mean</th></tr><tr><th>Teq</th><th>T</th><th>Tq</th><th>Teq</th><th>T</th><th>Tq</th><th>Teq</th><th>T</th><th>Tq</th><th>Teq</th><th>T</th><th>Tq</th></tr></thead><tbody><tr><td>Land + Ocean</td><td>1.57<br>[1]</td><td>0.72<br>[1]</td><td>0.85<br>[1]</td><td>1.65<br>[1]</td><td>0.67<br>[1]</td><td>0.98<br>[1]</td><td>-</td><td>-</td><td>-</td><td>1.61±0.06</td><td>0.70±0.04</td><td>0.92±0.09</td></tr><tr><td>Land only</td><td>1.80<br>[1]</td><td>1.06<br>[1]</td><td>0.73<br>[1]</td><td>1.90<br>[1]</td><td>1.01<br>[1]</td><td>0.89<br>[1]</td><td>2.04<br>[1]</td><td>0.96<br>[1]</td><td>1.13<br>[1]</td><td>1.91±0.12</td><td>1.01±0.05</td><td>0.92±0.20</td></tr><tr><td>Ocean only</td><td>1.46<br>[1]</td><td>0.58<br>[1]</td><td>0.88<br>[1]</td><td>1.55<br>[1]</td><td>0.52<br>[1]</td><td>1.02<br>[1]</td><td>-</td><td>-</td><td>-</td><td>1.51±0.06</td><td>0.55±0.04</td><td>0.95±0.10</td></tr></tbody></table>
This amplification is consistent with expectations of global-mean *RH* remaining approximately constant: the (exponential) increase in saturation vapor pressure with warming means that *Teq* must increase by more than *T* unless *RH* decreases sufficiently to leave *Tq* unchanged.

Considering spatial variation, both reanalyses agree that 2024 was the highest on record for *Teq*, *Tq*, and *T* over the oceans and over land (Table 2.4). For these datasets, the 2024 *T* anomaly was larger over land (1.01±0.05°C) than the ocean (0.55±0.04°C), whereas the *Tq* anomaly was more similar between land and ocean (0.92±0.20°C versus 0.95±0.10°C). As *Teq* is a combination of *T* and *Tq*, the land-ocean contrast for the 2024 anomaly (1.91±0.12°C versus 1.51±0.06°C) was also muted compared to *T*. Latitudinally, *Teq* anomalies in 2024 exhibited a more complex pattern than *T* (characterized by strong amplification in the Arctic; Figs. 2.14i-1). *Teq* had anomalies in the tropics and northern subtropics almost equal to those in the Arctic due to the much larger *Tq* anomalies at lower latitudes.

In general, 2024 *Tq* anomalies were therefore more likely to exceed *T* over the oceans and towards the equator (Figs. 2.14g,h,l). This pattern is consistent with theoretical expectations of *Tq* increasing by greater amounts relative to *T* in regions of higher baseline specific humidity

<figure><img src="image_59.png" /><figcaption>Fig. 2.14. Anomalies for (top to bottom): 2-m air temperature (*T*), latent temperature (*Tq*), equivalent temperature (*Teq*), and *T*-*Tq* according to ERA5 (left; [a],[c],[e],[g]) and the Japanese Reanalysis for Three Quarters of a Century (JRA-3Q) (right; [b],[d],[f],[h]) global (land and ocean). Zonal means for each variable are shown in the rightmost column (i)–(l) restricted to 85°S–85°N. Blue regions in the bottom row (*T*–*Tq*) experienced a larger 2024 anomaly in the latent heat term (*Tq*); red regions had a larger *T* anomaly.</figcaption></figure>
(Matthews et al. 2022). It also highlights the importance of using *Teq* alongside *T* if hotspots in total atmospheric heat accumulation are to be correctly identified.

There were, however, exceptions in 2024 to the patterns of change in *T* and *Tq* expected from the baseline climates, with perhaps the most notable in Amazonia and North Africa (Figs. 2.14g,h). Under the assumption of constant *RH*, Amazonia (with relatively high baseline *Tq*) is one of the regions anticipated to experience larger changes in *Tq* than *T* under warming; the expectation is reversed for the relatively dry climate of North Africa (Matthews et al. 2022). The larger 2024 *T* anomalies in Amazonia and greater *Tq* anomalies in parts of North Africa are therefore consistent with reported drought conditions in Amazonia in 2024 (Maciel et al. 2024) and the extreme wet season rainfall in the Sahel (Pinto et al. 2024).

In summary, 2024 was a record-breaking year for *Teq*, with all datasets unanimous in their agreement that it was easily the highest over land and ocean since at least 1979.
## 1. PERMAFROST TEMPERATURE AND ACTIVE-LAYER THICKNESS

—J. Noetzli, H. H. Christiansen, T. Gallemann, M. Gugliemin, F. Hrbáček, G. Hu, K. Isaksen, F. Magnin, P. Pogliotti, S. L. Smith, L. Zhao, and D. A. Streletskiy

Ground remaining at or below 0°C for two or more years—known as permafrost—occurs in polar and high mountain regions. Its warming and degradation can have important impacts on the landscape, ecosystems, infrastructure, and natural hazards. Widespread permafrost warming, thickening of the active layer (i.e., the seasonally thawed layer above the permafrost), and ground ice loss continued to be observed in 2024. Globally, permafrost warming rates at 10-m–20-m depth in cold permafrost (<-2°C) reach up to 1°C decade⁻¹. Warming rates are significantly reduced (<0.3°C decade⁻¹) in warm ice-bearing permafrost due to latent heat effects (Noetzli et al. 2024a; Smith et al. 2022; Zhao et al. 2024). Therefore, ground temperatures close to 0°C in ice-rich permafrost can remain nearly stable for years as ground ice is melting. Ground ice loss was observed in the Arctic (O'Neill et al. 2023; Streletskiy et al. 2025), European mountains (Etzelmüller et al. 2020; Mollaret et al. 2019), and the Qinghai–Tibet Plateau (QTP; Wang et al. 2023; Zou et al. 2024). Decadal active-layer thickening ranges from centimeters in continuous permafrost in Arctic sediments, to tens of centimeters in discontinuous bedrock permafrost in polar regions (Smith et al. 2024), southern Scandinavia (Etzelmüller et al. 2023), and the QTP (Zhao 2024; Hu 2024), to meters in bedrock or talus (scree) slopes in the European Alps (PERMOS 2024; Magnin et al. 2023).

Arctic permafrost has warmed by <0.3°C decade⁻¹ in warmer permafrost to 0.8°C decade⁻¹ in cold permafrost (Smith et al. 2024; see section 5j). Permafrost temperatures in 2024 were generally higher than in 2023. They were the highest on record at 8 of 25 sites reporting (7 in North America and 1 on Svalbard). In the Beaufort–Chukchi region (northern Alaska and Canadian Mackenzie Valley), higher permafrost temperatures in 2024 reflect higher surface air temperature (SAT) in 2023 that followed a short cooling period. Permafrost temperatures in high Arctic Svalbard increased again in 2024 following a cooling period after 2019 (Isaksen et al. 2022) and were close to previous values. Arctic permafrost is described in detail in section 5j.

On the Antarctic Peninsula, the annual shallow ground temperature in 2024 was 0.3°C below the 2011–20 mean on James Ross Island (Hrbáček et al. 2023) and within 0.1°C in the South Shetlands (de Pablo et al. 2024).

Permafrost in European mountains changed at rates between -0.01°C decade⁻¹ and +1.77°C decade⁻¹ (mean 0.41°C decade⁻¹) at 10-m depth during 2013–22 based on 50 time series, where permafrost was present at this depth for at least part of the decade (Noetzli et al. 2024b). Warming

was >0.7°C decade⁻¹ at 20% of these sites, which are in cold polar or high-elevation locations or where permafrost has recently disappeared. At 20-m depth, rates are generally lower (up to 0.7°C decade⁻¹) due to the delayed warming with increasing depth. In 2024, permafrost temperatures in the European Alps reached record highs at 10-m depth for most of the sites reporting (Fig. 2.15). This resulted from three consecutive exceptionally warm years and an early snow cover in winter 2024 following a warm autumn (MeteoSwiss 2025; PERMOS 2025). In northern Scandinavia, a record-warm summer 2024 led to strong permafrost warming at 10-m depth.

<figure><img src="image_60.png" /></figure>

**Fig. 2.15.** Mean annual ground temperature (°C) measured in European permafrost boreholes in the Alps, Scandinavia, and Svalbard at a depth of ~10 m. Maximum values for each time series are highlighted by a square. (Data sources: Norway [NO] and Svalbard [SJ]: Norwegian Meteorological Institute and the University Centre in Svalbard [UNIS]; Switzerland [CH]: Swiss Permafrost Monitoring Network [PERMOS]; France [FR]: Updated from Magnin et al. 2023; Italy [IT]: Updated from Pogliotti et al. 2023 and Guglielmin, M. unpublished data. Germany [DE]: Bavarian Environment Agency.)
At Iskoras in northern Norway, permafrost thawed to a depth of 24 m, with >15 m of permafrost lost since 2008 (Etzelmüller et al. 2023).

In the central Asian QTP, significant permafrost warming for 2005–23 is reported for six sites at rates between 0.04°C decade⁻¹ and 0.4°C decade⁻¹ at 10-m depth (Fig. 2.16) and between 0.02°C decade⁻¹ and 0.25°C decade⁻¹ at 20-m depth.

Active-layer thickness (ALT) increase in the Arctic continued in 2024 and was more pronounced in areas of discontinuous permafrost than in areas of continuous permafrost (Fig. 2.17; section 5j). In Interior Alaska and Mackenzie Valley, ALT was larger in 2023 and 2024 than in 2022. In Greenland, it was larger than average in 2024. In Svalbard, ALT reached an all-time high, following the third summer in a row with unprecedented high summer SAT. ALT in the European North and West

Siberia was lower in 2024 than in 2023, but substantially above the 2000–20 mean. For the limited sites reporting in East Siberia and Chukotka, the ALT decrease over the past five years continued, with values that were lower in 2024 than in 2023 and below the 2000–24 mean.

On the Antarctic Peninsula, the maximum ALT since the start of measurements in 2014 was registered in 2023. In 2024, ALT was only slightly above the long-term mean and the lowest observed since 2020.

In Europe's mountains, ALT reached or exceeded the 2022/23 record values and continued the marked thickening of the past three years. During the period 2000–20, ALT increased by meters at several sites, reaching values beyond the climatic variability (Fig. 2.17b). At Schilthorn, Swiss Alps, ALT tripled to >13 m during 1998–2022, and the ground did not re-freeze in winter 2024 (PERMOS 2025).

ALT near Kunlun mountain pass (QTP) increased by 20.2 cm decade⁻¹ during 1981–2023 at 10 sites, following a significant SAT increase during that period.

<figure><img src="image_61.png" /><figcaption>Fig. 2.16. Ground temperature (°C) measured at 10-m depth in the Qinghai–Tibet Plateau (QTP) in the period 2005–23. (Data source: Cryosphere Research Station on Qinghai-Xizang Plateau, Chinese Academy of Sciences [CAS].)</figcaption></figure>

<figure><img src="image_62.png" /><figcaption>Fig. 2.17. Standardized index of active-layer thickness (ALT) relative to 2000–20. (a) Arctic regions: Beaufort–Chukchi Sea–Arctic Alaska and Mackenzie Delta region (BCS), Interior Alaska and central Mackenzie Valley, Northwest Territories (IAK_CMV), Barents Sea region–West Siberia (BAR_WS), Central Siberia (CENT_SIB), East Siberia (EAST_SIB); (b) Mountain regions: Norwegian mountains (MNT_NOR), Swiss Alps (MNT_SWI), Italian Alps (MNT_IT), French Alps (MNT_FRA), Qinghai–Tibet Plateau (MNT_QTP); and (c) Antarctic: southern Victoria Land (ANT_SVL), Antarctic Peninsula (ANT_PEN), East Antarctic (ANT_EAST). Note that only data from 30% of the Russian sites are reported. (Data source: Circumpolar Active Layer Monitoring [CALM].)</figcaption></figure>
## 2. ROCK GLACIER VELOCITY

—C. Pellet, X. Bodin, D. Cusicanqui, R. Delaloye, A. Kääb, V. Kaufmann, E. Thibert, S. Vivero, and A. Kellerer-Pirklbauer

Rock glaciers are debris landforms generated by the creep of perennially frozen ground (permafrost). Their velocity evolutions are indicative of changes in the thermal state of permafrost and associated ground hydrological characteristics (RGIK 2023a). An overall increasing trend of rock glacier velocity (RGV) has been observed in mountain ranges worldwide since the 1950s (Pellet et al. 2024). In 2024, RGVs consistently increased in the European Alps compared to 2023, and in the Dry Andes, RGVs remained at a high level, with values similar to 2020. RGVs recently compiled in the United States further confirm this general trend of RGV increase (Kääb and Røste 2024), which is consistent with the increase of permafrost temperatures (section 2c1) to which RGV respond more or less synchronously (e.g., Staub et al. 2016).

In the European Alps, 2024 was the second-warmest hydrological year on record based on the average of five high-elevation stations (+1.52°C; Fig. 2.18a) yielding a general increase of RGVs. Compared to 2023, the highest RGV increase occurred in the Swiss Alps (+80.8% at Gemmi/Furggentälti and +80.7% at Grosses Gufer), whereas a +16.9% increase was observed in the French Alps at Laurichard as well as +3.4% and +5.1% increases in the Austrian Alps at Dösen and Hinteres Langtalkar, respectively (Fig. 2.18b). These observations are consistent with the permafrost temperature observations (section 2c1) as confirmed by the increasing temperatures measured in 2024 at 10-m depth on the rock glacier Murtèl in eastern Switzerland (Fig. 2.18a). The regional differences in magnitude of velocity increase is related to landform-specific characteristics combined with the spatial variability of snow conditions, namely early onset and well-above-average snow height throughout the winter in Switzerland and France (preventing any cooling of the ground; PERMOS 2025) as well as early onset followed by below-average snow heights in Austria (enabling limited cooling). The reported RGV observations in 2024 in the European Alps are consistent with the general acceleration trend observed at all sites since the 1950s (Kellerer-Pirklbauer et al. 2024).

In the Dry Andes, RGVs observed during 2023/24 show increases of +13.7% and +1.3% on El Cachito and Las Tolas, respectively, whereas a -15.9% decrease is observed on Tapado compared to 2019/20 (Fig. 2.18c). Velocities reached maximum values at El Cachito and remained at a high level compared to the entire time series on Las Tolas and Tapado. The overall increase observed since the 2000s is further confirmed by a recent study on Largo rock glacier (Fig. 2.18c; Cusicanqui et al. 2024) and is consistent with the slight air temperature increase observed in the region since 1976 (Vivero et al. 2021).

In Central Asia, RGVs observed on four landforms since the 1950s exhibit a general increase, with a marked acceleration in the period 2010–20 (Fig. 2.18d). This evolution is consistent with increasing air temperatures in the region (Azisov et al. 2022; Sorg et al. 2015).

In the United States, RGVs compiled on six rock glaciers show an overall increase since the first available measurements in the 1950s (Fig. 2.18e; Kääb and Røste 2024). This trend is consistent with the strongly increasing air temperature observed in that region (Kääb and Røste 2024).

RGV refers to velocities related to permafrost creep, which has to be understood as a combination of internal deformation of the frozen ground (creep stricto sensu) and shearing in one or more layers at depth (shear horizon; RGIK 2023b). RGVs are mostly related to the evolution of ground temperature and liquid water content between the upper surface of permafrost and the shear horizon (Cicoira et al. 2019; Staub et al. 2016). RGV increase and decrease positively correlate with temperature change. Despite differences in size, morphology, topographical, climatic, and geological settings, as well as velocity ranges, consistent regional RGV evolutions have been highlighted in several studies (see Hu et al. 2025). RGV time series are produced using both in situ and optical remote sensing (airborne and spaceborne) measurements. Surface displacements are computed based on matching between images or digital elevation models taken at different times, with the resulting accuracy strongly depending on the characteristics of the input data (Kääb et al. 2021; Vivero et al. 2021). Surface displacements are averaged for a cluster of points/pixels selected within areas considered as representative of the downslope movement
of the rock glacier (RGIK 2023b). The in situ measurements consist of annually repeated terres-
trial geodetic surveys of the positions of selected boulders (10–100 per landform), yielding
displacement observation with an average accuracy of mm to cm (Lambiel and Delaloye 2004;
Thibert and Bodin 2022).

<figure><img src="image_63.png" /><figcaption>Fig. 2.18. Rock glacier velocity and climate: (a) air temperature in the European Alps and ground temperature in the Swiss Alps (°C), (b)–(e) rock glacier velocities (m yr⁻¹) at selected sites in the (b) European Alps, (c) Dry Andes (updated from Vivero et al. 2021 and Cusicanqui et al. 2024), (d) Central Asia (updated from Kääb et al. 2021), and (e) United States (adapted from Kääb and Røste 2024). Rock glacier velocities are based on in situ geodetic surveys ([b], since 2000s) or photogrammetry ([b]–[e]) in the context of long-term monitoring. In situ hydrological mean annual permafrost temperature measured at 10-m depth (blue line) at Murtèl Corvatsch (blue triangle on Europe map) and air temperature: composite anomaly to the 1991–2020 base period (bars) and composite 20-year running mean (solid line) at Besse (France [FR]), Grand Saint-Bernard (Switzerland [CH]), Saentis (CH), Sonnblick (Austria [AT]), and Zugspitze (Germany [D], blue diamonds on Europe map). (Sources: Météo-France, Deutscher Wetterdienst [DWD], MeteoSwiss, GeoSphere Austria, Swiss Permafrost Monitoring Network [PERMOS], University of Fribourg, University of Graz, Graz University of Technology, Université Grenoble Alpes National Institute of Agricultural Research [INRAE], University of Oslo).</figcaption></figure>
### 3. ALPINE GLACIERS

—M. Pelto

In 2024, all 58 global reference glaciers reported a negative annual mass balance. This is only the second year in the 1970–2024 period with all negative annual balances, following 2023. The global average annual mass balance based on equal weighting of 19 regions is –1.30 m water equivalent (w.e.), the most negative value in the record (Fig. 2.19).

The 2024 dataset of submitted glaciological observations includes 142 glaciers from six continents and 27 nations, with 140 reporting a negative balance and 2 a positive balance. In 2024, the mean annual mass balance of the 58 global reference glaciers was –1.44 m w.e. and –1.36 m w.e. for all 142 reporting glaciers. This is a similar result to 2023, which saw a mean reference glacier balance of –1.62 m w.e. and –1.35 m w.e. for all 116 reporting glaciers.

The 2024 regionalized global average of –1.30 m w.e. exceeds the previous most negative year in 2023, which saw a regionalized global average of –1.25 m w.e. This makes 2024 the 37th consecutive year with a global alpine mass balance loss and the 15th consecutive year with a regionalized global mass balance below –0.5 m w.e. The acceleration of mass balance loss indicates that alpine glaciers are not approaching equilibrium. The acceleration of mass balance loss is apparent regardless of datasets used to determine it, including glaciological, geodetic, altimetry, and gravimetric observations (The GlaMBIE Team 2025). The intercomparison assessment identified that global glaciers annually lost 273+26 gigatons (Gt) in mass from 2000 to 2023, with loss having been 36% greater in the second half than in the first half of this period (The GlaMBIE Team 2025).

In the European Alps, all 49 glaciers reported negative mass balances, with 45 losing over 1 m w.e. All 10 Icelandic glaciers had negative balances. In Svalbard, all seven had negative balances exceeding an exceptional

loss of 1.25 m w.e. This was the result of near complete snow cover loss across most glaciers (Fig. 2.20) following record temperatures in August (see section 7f5 for details). Twelve of the 13 glaciers from Norway and Sweden had mass losses of more than 1.0 m w.e.

Across High Mountain Asia, 20 of 21 glaciers, reporting from seven nations, had negative balances. The highest average losses were in the Himalayas of Nepal and the lowest in the Pamir Range of Tajikistan.

In the Andes Mountains of South America, all 14 glaciers, reporting from five nations, had negative balances. Conejeras Glacier (Colombia), following a 5.04 m w.e. loss in 2023, was declared extinct in 2024. The daily hydrograph below this glacier changed from a predominance

<figure><img src="image_64.png" /></figure>

Fig. 2.19. Time series of regionalized average global mean annual glacier mass balance of alpine glaciers from 1970–2024 in m w.e., as determined by the World Glacier Monitoring Service. Annual mass balance is shown in red bars and annual cumulative mass balance is indicated by black dots.

<figure><img src="image_65.png" /></figure>

Fig. 2.20. Snow-free ice caps on Edgeoya Island, Svalbard, in Sentinel-2 Short-Wave Infrared RGB Composite imagery from 28 August 2024. Green represents vegetation, brown represents rock and soil, black represents water, white to very light blue represents snow, and darker grays and blues represent ice and firn. Glaciers across Svalbard were largely snow-free, leading to consistent mass balance losses exceeding –1.25 m w.e.
of days with a purely melt-driven hydrograph from 2006 to 2016 to an increase in the frequency of days with flows less influenced by melt after 2016 (Morán-Tejeda et al. 2018).

All 16 glaciers in North America had negative balances. All four glaciers in Arctic Canada had mass balance losses under 1 m w.e. In western Canada and Washington and Montana (United States), all 16 glaciers reporting had losses exceeding 1 m w.e. The Ice Worm Glacier (Washington) was listed as extinct in 2023 after 40 years of continuous observations (Pelto 2024). In 2024, loss from the relict ice (ice that is no longer moving or part of a glacier) was 2.4 m and melt runoff below the glacier had decreased similar to Conejeras Glacier (Pelto and Pelto 2025). In Alaska, all three glaciers had mass balance losses. Davies et al. (2024) examined the Juneau Icefield, the most observed icefield in Alaska in terms of mass balance, and found an acceleration of mass loss with a doubling after 2010 compared to 1979–2010.

Alpine annual mass balance glaciological observations are reported to the World Glacier Monitoring Service (WGMS) by national representatives with a 1 December annual submission deadline. WGMS reference glaciers have at least 30 continuous years of mass balance observation. Benchmark glaciers have at least a 10-year mass balance record and are in regions that lack sufficient reference glaciers. The combination of benchmark and reference glaciers is used to generate regional averages (WGMS 2023). Global values are calculated using a single averaged value for each of 19 mountain regions, limiting bias from observed regions (WGMS 2023). As this dataset expands, the annual values are reanalyzed and updated.

## 4. LAKE ICE COVER

—J. Culpepper, S. Sharma, R. I. Woolway, and J. E. Ollinik

Northern Hemisphere (NH) lakes tended toward later ice formation, earlier ice breakup, and shorter duration during the winter of 2023/24, similar to long-standing trends (Sharma et al. 2021). However, there was regional variability in ice patterns between North America and Europe, likely resulting from stronger warm anomalies in winter air temperatures in North America (see section 7b).

On average, NH lakes froze 3.6 days later, broke up 6.1 days earlier, and ice duration was 10 days shorter, based on ERA5 reanalysis data and compared to the 1991–2020 base period (Figs. 2.21a–c). Most regions experienced shorter ice duration, with the exception of Scandinavia, which saw longer duration owing partially to colder winter air temperature anomalies, particularly in the late autumn and early winter (October–January; Figs. 2.21c,d).

On average, during the 2023/24 winter, in situ lake ice observations (n = 123) revealed that ice-on was 7.6 days later, ice-off was 17.5 days earlier, and ice duration was 24 days shorter relative to the 1991–2020 base period (Figs. 2.22a–c). The lakes in North America had on average 42.4 fewer days of ice cover, whereas lakes in Finland and Sweden experienced 13.5 more days

<figure><img src="image_66.png" /><figcaption>Fig. 2.21. Anomalies (days) compared to 1991–2020 base period in lake ice cover across the Northern Hemisphere during the winter of 2023/24 for the (a) ice formation period (negative [positive] values indicate earlier [later] ice formation), (b) ice breakup period (negative [positive] values indicate earlier [later] ice breakup), and (c) ice duration period. For duration (c), negative (positive) values indicate shorter (longer) ice cover. (d) The surface temperature anomalies (°C) for average temperatures between Nov 2023 and Apr 2024, where negative (positive) values are colder (warmer) temperatures.</figcaption></figure>
of ice cover. El Niño-related temperature pattern conditions (Fig. 2.21d) strongly decreased ice coverage in North American lakes. Furthermore, Lake Suwa in Japan (36.0499°N, 138.0857°E) did not freeze, continuing a pattern of intermittent freezing. Lake Suwa has frozen in only 17 of the last 30 years, but has frozen every year between 2021 and 2023. Notably, Lake Suwa froze in 87 years in the twentieth century, although the majority of the ice-free years occurred after the 1970s.

Mountain lakes in North America and Europe experienced an average of 6.8 fewer days of ice cover (n = 13), resulting from ice formation (n = 14) that was 0.8 days earlier and ice breakup (n = 13) that was 6.9 days earlier (Fig. 2.22). Mountain lakes are a separate subset of in situ lakes identified using digital elevation models, which have an elevation of at least 300 m a.s.l. Ice loss can be more extreme in these lakes. For example, Lake Lunz only experienced 2 days of ice cover in 2024, compared to an average of 62 days between 1990 and 2019 (Kainz et al. 2017). Despite warmer temperatures during November through April, ice breakup was later in North American mountain lakes. Anomalously high snow cover (section 2c5) in the western United States likely drove delayed ice breakup in the California (Chandra et al. 2023) and Colorado (Caine et al. 2024) lakes included in this dataset.

The Laurentian Great Lakes had 31.8% less maximal ice coverage during the 2023/24 winter relative to the winters of 1991–2020. Lake Superior had 42.5% less ice coverage, followed by Lakes Erie (40.3%), Huron (37.3%), Ontario (20.9%), and Michigan (18.1%; Fig. 2.23). The Great Lakes reached their maximal ice coverage of 16% on 22 January, 31 days earlier than average. Notably, there was only 2.7% ice coverage across all of the Great Lakes on 11 February, the lowest ice coverage measured in mid-February since 1973. The Great Lakes region was characterized by generally warm winter air temperatures in 2023/24, with only a brief period of cold air temperatures in January 2024 (NOAA 2024). After the year 2000, an oscillatory pattern figures prominently in the time series (Fig. 2.23). Research suggests that a combination of the El Niño–Southern Oscillation (ENSO) and North Atlantic Oscillation (NAO) contribute to the variability in ice cover conditions in the Laurentian Great Lakes. For example, the Great Lakes have lower ice cover during strong La Niña events and the positive phase of the NAO (Bai et al. 2012). Moreover, NAO and ENSO can also interact to influence ice cover in a winter (Bai et al. 2012).

<figure><img src="image_67.png" /><figcaption>Fig. 2.22. The (a) ice-on, (b) ice-off, and (c) ice duration anomalies (days) for Northern Hemisphere lakes from 1990 to 2024 compared to the 1991–2020 base period for in situ observations (gray), mountain lake observations (blue), and ERA5 (black). Negative (positive) values indicate earlier (later) ice-on, earlier (later) ice-off, and shorter (longer) ice duration.</figcaption></figure>

<figure><img src="image_68.png" /><figcaption>Fig. 2.23. Anomalies of the maximum ice cover extent (%) of the Laurentian Great Lakes for the period of 1973 to 2024 in relation to the 1991–2020 base period. The colored lines indicate individual lake anomalies (e.g., Erie anomaly in blue), while the black line shows the average anomaly. Negative (positive) values indicate less (more) maximum ice cover extent.</figcaption></figure>
Established monitoring networks and citizen scientists contributed in situ observations for 123 lakes across Canada (n = 5 lakes), the United States (n = 88), Finland (n = 27), Sweden (n = 1), and Japan (n = 1) (Sharma et al. 2022). Mountain lake ice cover was derived from a similar network of scientists through the NH in North America (n = 8 lakes) and Europe (n = 10; Caine et al. 2024; Chandra et al. 2023; Kainz et al. 2017). Anomalies for each in situ lake were calculated as the difference between the 2023/24 ice value (i.e., ice-on, ice-off, duration) and the average of the 1991–2020 base period. Information on lake ice phenology was downloaded from ERA5 (Hersbach et al. 2020). Annual maximum ice coverage (%) data for each of the Laurentian Great Lakes were acquired for 1973–2024 from the NOAA Great Lakes Environmental Research Laboratory. Notably, the definitions of ice-on and ice-off varied by lake (e.g., complete ice cover, the date most of the lake is frozen [90%] or when most of the lake is ice-free [10%], the first or last time a boat could travel through two points), but did not vary over time for each lake (Sharma et al. 2022).

# 5. NORTHERN HEMISPHERE CONTINENTAL SNOW COVER EXTENT

—D. A. Robinson and T. W. Estilow

Annual snow cover extent (SCE) over Northern Hemisphere (NH) land averaged 23.9 million km² in 2024. This was 1.0 million km² less than the 1991–2020 mean and 1.2 million km² below the mean of the full period of record (1967–2024; Table 2.5). Overall, 2024 had the third-least-exten-

sive cover on record. Twelve-month running mean SCE departures over all NH lands have not been as low as they were at the end of 2024 since June 2007, and before then July 1990 (Fig. 2.24a). Monthly SCE in 2024 ranged from a maximum of 46.9 million km² in January to a minimum of 2.6 million km² in August. Annual-mean SCE in North America (NA) was the least extensive on record in 2024, 0.1 million km² less than that of 1990; 2024 is now the second-least-extensive year on record. In terms of 12-month running means, the June 2024 value was the lowest since May 1968. Weekly NH SCE in 2024 was below long-term means for all but a few weeks from a winter maximum SCE in early January through the melt season. Autumn SCE was close to normal to begin the season, later becoming more erratic week to week from November into December (Fig. 2.24b).

In January, the NH SCE was in the middle tercile of the 58-year record. From February onward through spring, NH ranked in the lower tercile, from 1st to 19th least-extensive, with April having been record low. NA SCE in January was the 16th most extensive in January, while Eurasia was 18th least extensive. Both continents ranked from 3rd to 20th least extensive in each month from February to June. NA SCE was the third least extensive in both February and April. The 2024 snow season began in autumn with the 17th-most-extensive SCE over Europe in September and 18th in October. Meanwhile, snow was slow to appear over NA, with the 5th- and 13th-least-extensive SCE in September and October, respectively.

<figure><img src="image_69.png" /></figure>

**Fig. 2.24.** (a) Twelve-month running anomalies of monthly snow cover extent (SCE) over Northern Hemisphere (NH) lands as a whole and Europe (EUR) and North America (NA) separately plotted on the seventh month using values from Nov 1966 to Dec 2024. Anomalies are calculated from NOAA snow maps. Mean NH SCE is 25.1 million km² for the full period of record. Monthly means for the period of record are used for nine missing months during 1968, 1969, and 1971 to create a continuous series of running means. Missing months fall between Jun and Oct. (b) Weekly NH SCE for 2024 (black) plotted with the mean (gray dashed line), maximum (purple), and minimum (green) SCE for each week. Mean weekly SCE and extremes are calculated using the 58-year record from Jan 1967 to Dec 2024. Weekly means for the period of record are used for 4, 21, and 12 missing weeks during 1968, 1969, and 1971 respectively. Weekly data granules represent SCE for each seven-day period ending on Monday.
NH SCE was the 19th least extensive in September and 25th most extensive in October. November and December SCE was 27th and 12th least extensive, respectively.

The contiguous United States' (U.S.) SCE was above normal, 16th most extensive, to start 2024, then rapidly fell to the 2nd least extensive in February. March through May ranged from 26th to 14th least extensive. Across Canada (CAN), the highest ranking for the winter and spring was 22nd least extensive in January, with each month from February through June ranging from 4th to 10th least extensive. In September, CAN ranked fourth least extensive and the U.S. was almost snow free. The U.S. and CAN SCE ranked, respectively, as the 17th and 16th least extensive in October. CAN also observed the 16th-least-extensive SCE in November, while the U.S. SCE increased to 24th most extensive. Snow conditions for the two nations reversed in December, with the U.S. seeing its 8th-least-extensive ranking and CAN its 15th most extensive.

SCE is calculated at the Rutgers Global Snow Lab (GSL) from daily SCE maps produced by meteorologists at the U.S. National Ice Center, who rely primarily on visible satellite imagery to construct the maps (Estilow et al. 2015). Maps depicting daily, weekly, and monthly conditions, anomalies, and climatologies may be viewed at the GSL website (https://snowcover.org).

<table><caption>Table 2.5. Monthly and annual climatological information for Northern Hemisphere (NH), Eurasia (EUR), and North America (NA) snow cover extent (SCE) between Nov 1966 and Dec 2024. Included are the numbers of years with data used in the calculations, NH anomalies, NH means, NH standard deviations (Std. Dev.), and ranks. Areas are in millions of square kilometers. 1968, 1969, and 1971 have one, five, and three missing months, respectively, and thus are not included in the annual (Ann) calculations. NA includes Greenland. Ranks are from most to least extensive (least to most in parentheses).</caption><thead><tr><th>Time Period</th><th>Yrs</th><th>NH Anomaly</th><th>NH Mean</th><th>NH Std. Dev.</th><th>2024 NH Rank</th><th>2024 EUR Rank</th><th>2024 NA Rank</th></tr></thead><tbody><tr><td>Jan</td><td>58</td><td>-0.2</td><td>47.1</td><td>1.5</td><td>32 (27)</td><td>41 (18)</td><td>16 (43)</td></tr><tr><td>Feb</td><td>58</td><td>-2.1</td><td>45.9</td><td>1.8</td><td>50 (9)</td><td>39 (20)</td><td>56 (3)</td></tr><tr><td>Mar</td><td>58</td><td>-2.0</td><td>40.4</td><td>1.8</td><td>51 (8)</td><td>46 (13)</td><td>45 (14)</td></tr><tr><td>Apr</td><td>58</td><td>-2.6</td><td>30.4</td><td>1.7</td><td>58 (1)</td><td>49 (10)</td><td>56 (3)</td></tr><tr><td>May</td><td>58</td><td>-1.9</td><td>19.0</td><td>2.0</td><td>45 (14)</td><td>40 (19)</td><td>53 (6)</td></tr><tr><td>Jun</td><td>57</td><td>-2.6</td><td>9.2</td><td>2.5</td><td>46 (12)</td><td>44 (14)</td><td>48 (10)</td></tr><tr><td>Jul</td><td>55</td><td>-1.1</td><td>3.8</td><td>1.2</td><td>45 (11)</td><td>45 (11)</td><td>44 (12)</td></tr><tr><td>Aug</td><td>56</td><td>-0.3</td><td>2.9</td><td>0.7</td><td>33 (24)</td><td>51 (6)</td><td>23 (34)</td></tr><tr><td>Sep</td><td>56</td><td>-0.5</td><td>5.4</td><td>0.9</td><td>40 (17)</td><td>17 (40)</td><td>54 (3)</td></tr><tr><td>Oct</td><td>57</td><td>+0.3</td><td>18.6</td><td>2.6</td><td>25 (33)</td><td>18 (40)</td><td>47 (11)</td></tr><tr><td>Nov</td><td>59</td><td>0.0</td><td>34.4</td><td>2.1</td><td>32 (28)</td><td>30 (30)</td><td>33 (27)</td></tr><tr><td>Dec</td><td>59</td><td>-1.0</td><td>43.7</td><td>1.8</td><td>47 (13)</td><td>39 (21)</td><td>44 (16)</td></tr><tr><td>Ann</td><td>55</td><td>-1.2</td><td>25.1</td><td>0.8</td><td>53 (3)</td><td>47 (9)</td><td>55 (1)</td></tr></tbody></table>
Global near-surface humidity remained exceptionally high in 2024, with a record-wet annual-mean specific humidity (q) anomaly over both land and ocean (Fig. 2.25; Table 2.6). This was the case for all data products, with record anomalies ranging from 0.32 g kg⁻¹ (ERA5) to 0.58 g kg⁻¹ (MERRA-2 masked to Met Office Hadley Centre Integrated Surface Dataset of Humidity [HadISDH] coverage) over land and 0.35 g kg⁻¹ (ERA5) to 0.56 g kg⁻¹ (HadISDH) over ocean. In all cases, 2024 land and ocean q was at least 0.1 g kg⁻¹ wetter than in 2023. Global-mean relative humidity (RH), excluding MERRA-2, remained below the 1991–2020 baseline over land, between -0.13%rh to -0.71%rh (note that %rh is the unit for relative humidity, which is a percentage of how saturated the air is), meaning that air saturation is still low. However, land RH was not as dry as recent years in ERA5 and HadISDH, including 2023. Ocean near-surface RH was at or above the long-term mean in 2024. It showed a greater level of saturation than in 2023 in all data products, continuing a moistening tendency from 2020/21 and becoming a record-humid year for HadISDH and the Japanese Reanalysis for Three Quarters of a Century (JRA-3Q). Unlike land and ocean q, as well as land RH, there is little temporal agreement between any of the data products. Hence, uncertainty in ocean RH is large.

The recent ocean RH increase was consistent with the large increase in ocean near-surface water vapor, as inferred by q. This was related to the presence of El Niño from June 2023 to May 2024, which pumped extra moisture into the air in response to the warmer air and sea temperatures. Additional potential drivers of the extraordinarily warm near-surface air temperature in 2023/24 are still being explored (Goessling et al. 2025), including ongoing greenhouse gas emissions, reduced aerosol emissions, unusual heat in the North Atlantic Ocean, reduced low-cloud cover (Goessling et al. 2025), approach to solar maximum (NASA 2024), and additional stratospheric water vapor from the Hunga eruption. If these factors have been sufficient to contribute to global mean temperature, then they will likely also be contributing to the large amount of water vapor (q) and greater saturation (RH) compared to recent years.

Notably, previous El Niño events of 1972/73, 1977/78, 1982/83, 1986/87, 1991/92, 1997/98, 2009/10, 2015/16, and even the weak 2020 event are detectable in the global mean land and ocean q records (Figs. 2.25a–d, 2.26a). Associated peaks in RH are present but less clear, especially for ocean RH, with some events apparent in some data products but not others (Figs. 2.25e–h, 2.26c). Other sources of variability, including observational errors and biases, also contribute to RH variability. The 2023/24 El Niño was weaker and shorter (in terms of Niño-3.4 region temperatures) than the 1982/83, 1997/98, and 2015/16 events, making the q and RH anomalies even more noteworthy. The 1977/78, 1986/87, 2009/10, and 2023/24 El Niños were preceded by protracted periods of La Niña, which likely suppressed humidity somewhat, resulting in apparently larger increases thereafter. This was evident

<figure><img src="image_70.png" /></figure>

Fig. 2.25. Global average surface humidity annual anomalies (g kg⁻¹ for [a]–[d] and %rh for [e]–[h]; 1991–2020 base period). For the in situ datasets, 2-m surface humidity is used over land, and ~10-m surface humidity over the oceans. For the reanalysis, 2-m humidity is used over the whole globe. For ERA5, ocean-series-only points over open sea are selected. ERA5 mask is a version of ERA5 limited to the spatial coverage of the Met Office Hadley Centre Integrated Surface Dataset of Humidity (HadISDH). 2-sigma uncertainty is shown for HadISDH capturing the observation, gridbox sampling, and spatial coverage uncertainty. Pink and blue shading highlights El Niño and La Niña years respectively, as identified by the NOAA Physical Sciences Laboratory Nino-3.4 index using the ±0.5°C threshold. (Sources: HadISDH [Willett et al. 2013, 2014, 2020]; ERA5 [Hersbach et al. 2020]; the Japanese Reanalysis for Three Quarters of a Century [JRA-3Q]; Kosaka et al. 2024]; MERRA-2 [Gelaro et al. 2017].)
for *q* land and ocean but inconsistent for *RH* land and ocean, with differences between data products. Masked versions of MERRA-2 and ERA5, matching HadISDH coverage, show consistently higher *q* anomalies for recent years than their full-coverage equivalents (Table 2.6). This suggests that HadISDH is undersampling regions with lower *q* anomalies, which tend to be the drier regions (Simpson et al. 2024). Note that MERRA-2 land *RH* is quite different from that of the other estimates; the reasons for this are an active area of investigation.

Gaps over the drier land regions in HadISDH are clear in Appendix Figs. A2.6 and A2.8, as is the limited ocean coverage, leaving the Southern Ocean and central and eastern tropical Pacific barely sampled. Widespread wet anomalies in *q* exceeded 1 g kg⁻¹ over northern Australia, South and North Korea, Japan, and their surrounding seas, as well as over India, the Caribbean and Central America, part of the North Pacific, and parts of northern tropical Africa. These were common to all data products (Plate 2.1h; Appendix Fig. A2.7) but with slight divergence over the North Pacific region. Anomalously large water vapor amounts were more widespread than

<table><caption>Table 2.6. Global mean surface-specific (*q*) and relative humidity (RH) anomalies (g kg⁻¹ and %rh, respectively) for 2024 and 2023. Note that no previous record is reported for ocean RH because a long-term trend has not been robustly established. Values with a thermometer icon (ⓘ') identify new record-high values and year of previous highest.</caption><thead><tr><th>Dataset</th><th>q (g kg<sup>-1</sup>)<br>2024 Global Mean<br>Anomaly</th><th>q (g kg<sup>-1</sup>)<br>2023 Global Mean<br>Anomaly</th><th>RH (%rh)<br>2024 Global Mean<br>Anomaly</th><th>RH (%rh)<br>2023 Global Mean<br>Anomaly</th><th>RH (%rh)<br>Record low<br>(Year of Record Low)</th></tr></thead><tbody><tr><td>HadISDH.land</td><td>0.49<br>(2023)</td><td>0.31</td><td>-0.13</td><td>-0.56</td><td>-0.79<br>(2019)</td></tr><tr><td>ERA5<br>Over Land</td><td>0.32<br>(2016)</td><td>0.17</td><td>-0.67</td><td>-1.05</td><td>-1.32<br>(2021)</td></tr><tr><td>ERA5<br>Over Land Masked</td><td>0.40<br>(2023)</td><td>0.22</td><td>-0.43</td><td>-1.02</td><td>-1.26<br>(2021)</td></tr><tr><td>MERRA-2<br>Over Land</td><td>0.46<br>(2023)</td><td>0.33</td><td>0.14</td><td>0.01</td><td>-1.21<br>(2002)</td></tr><tr><td>MERRA-2<br>Over Land Masked</td><td>0.58<br>(2023)</td><td>0.40</td><td>0.40</td><td>0.04</td><td>-1.10<br>(2002)</td></tr><tr><td>JRA-3Q<br>Over Land</td><td>0.38<br>(2023)</td><td>0.26</td><td>-0.71</td><td>-0.91</td><td>-0.93<br>(2021)</td></tr><tr><td>HadISDH.marine</td><td>0.56<br>(2023)</td><td>0.4</td><td>0.71<br>(1977)</td><td>0.06</td><td>-</td></tr><tr><td>ERA5<br>Over Ocean</td><td>0.35<br>(2023)</td><td>0.24</td><td>0.03</td><td>-0.08</td><td>-</td></tr><tr><td>ERA5<br>Over Ocean Masked</td><td>0.56<br>(2023)</td><td>0.39</td><td>0.07</td><td>-0.18</td><td>-</td></tr><tr><td>MERRA-2<br>Over Ocean</td><td>0.52<br>(2023)</td><td>0.42</td><td>0.44</td><td>0.37</td><td>-</td></tr><tr><td>MERRA-2<br>Over Ocean Masked</td><td>0.73<br>(2023)</td><td>0.55</td><td>0.39</td><td>0.14</td><td>-</td></tr><tr><td>JRA-3Q<br>Over Ocean</td><td>0.44<br>(2023)</td><td>0.34</td><td>0.33<br>(1959)</td><td>0.10</td><td>-</td></tr></tbody></table>
in 2023. Then, the El Niño warm tongue pattern, strong positive Indian Ocean dipole, and high North Atlantic sea surface temperature (SST) patterns were clear. This spreading of positive *q* anomalies is common to many El Niño years. However, the 2023/24 *q* anomalies, in the context of the historical record, were unusually widespread and large (Fig. 2.26a). Despite this, some dry anomalies persisted from 2023 over the Amazon, central and southern Africa, and Mexico, and were actually more widespread and intense.

Positive, more-saturated-than-normal *RH* anomalies were more widespread and stronger than in recent years (Fig. 2.26; Plate 2.1i; Appendix Figs. A2.7, A2.9). Northern and eastern Australia, India, eastern Mongolia and northeastern China, Kazakhstan, and northeastern North America over land were more humid than normal. The positive anomalies over the eastern tropical Pacific, northeast Atlantic, and North Pacific were also notable. As for *q*, more-arid-than-normal anomalies strengthened relative to 2023 over the Amazon, central and southern Africa, and Mexico. HadISDH differs from ERA5 and MERRA-2 by showing less intense negative *RH* anomalies.

<figure><img src="image_71.png" /><figcaption>Fig. 2.26. Latitudinal monthly mean anomalies of (a) specific humidity (g kg⁻¹) and (c) relative humidity (%rh) by month from Met Office Hadley Centre Integrated Surface Dataset of Humidity over Land and Ocean (HadISDH.blend). (b),(d) Decadal trends for each gridbox (dots) and latitude band mean (line) are fitted using an ordinary least-squares (OLS) linear regression with an autoregressive (1) correction following Santer et al. (2008), with gray shading representing the percentage of globe covered by observations (in gridboxes) at each latitude band. Latitude band means are only calculated where there are at least five gridboxes (5° × 5°).</figcaption></figure>
## 2. HUMID-HEAT EXTREMES OVER LAND

—K. M. Willett, R. M. Horton, Y. T. E. Lo, C. Raymond, C. D. W. Rogers, and D. Wang

Following an exceptional increase in the intensity and frequency of high humid heat in 2023, 2024 continued the upward trajectory (Fig. 2.27; Table 2.7). Maximum humid-heat intensity ($T_w$X; Table 2.7) was 0.5°C higher than the 1991–2020 average and only slightly lower than that of 2023 (0.6°C); where maximum humid-heat intensity is the annual median of the global land median monthly maximum wet-bulb temperature. Remarkably, high daily maximum humid-heat frequency ($T_w$X90p; Table 2.7) far exceeded the previous record, at 35.6 days above average versus 26.1 days in 2023; where high daily maximum humid-heat frequency is the annual sum of global-mean days per month with daily maximum wet-bulb temperature exceeding the local 90th percentile. These values are based on the gridded HadISDH Extremes (HadISDH/extremes.1.2.0.2024f; Willett 2023a,b,c; Willett et al. 2024) dataset, where monthly indices of daily maximum and minimum wet-bulb temperature are used as a measure of humid heat. Note that for the purposes of this review, “humid heat” is used as an energetic term that includes the contribution of temperature and moisture over the entire globe and annual cycle, rather than a term that focuses exclusively on regions and seasons where temperature and moisture are high.

For all specific thresholds of $T_w$X exceedance (Fig. 2.27d; Table 2.7), 2024 had record-high frequencies for the second consecutive year. For $T_w$X25, $T_w$X27, and $T_w$X29, these anomalies (with respect to the 1991–2020 base period) were 11.0 days, 13.8 days, and 1.7 days, respectively. $T_w$X31 anomalies were tied for record most frequent with both 2023 and 1998, at 0.2 days. In much of the tropics, almost every day of the year exceeds the $T_w$ = 25°C threshold. Hence, the maximum possible globally averaged anomaly for $T_w$X25 is constrained and, therefore, can be less than the anomalies for $T_w$X27.

In 2024, most global land regions experienced more intense ($T_w$X) and more frequent ($T_w$X90p) high daily maximum humid-heat days than the 1991–2020 average (Plates 2.1j,k). High daily maximum humid heat was particularly frequent over India, Southeast Asia, East Asia, Australia, the Caribbean, Central America, and Europe (Plate 2.1k). There were several small regions of negative anomalies, which are mostly consistent between $T_w$X and $T_w$X90p; these occurred notably east of the Caspian Sea, in Mongolia, around the Red Sea,

<figure><img src="image_72.png" /></figure>

Fig. 2.27. Global land annual anomaly time series of various daily maximum and minimum humid-heat indices from Met Office Hadley Centre Integrated Surface Dataset of Humidity Extremes (HadISDH/extremes), relative to a 1991–2020 base period. Decadal trends (significant at p<0.01) are also shown. Trends were fitted using an ordinary least-squares linear regression with an autoregressive (1) correction following Santer et al. (2008). (a) Anomaly of the annual median of the global median monthly maximum (black) and minimum (blue) wet-bulb temperature (°C). (b) Anomaly of the annual sum of days where maximum (black) and minimum (blue) wet-bulb temperature exceeds the locally defined daily 90th percentile. (c) Anomaly of the annual sum of days where maximum (black) and minimum (blue) wet-bulb temperature is lower than the locally defined daily 10th percentile. (d) Anomalies of the annual sums of days where the maximum wet-bulb temperature ≥25°C, 27°C, 29°C, and 31°C thresholds. Note that coverage is skewed towards the northern extratropical latitudes with large data gaps over Africa, as well as considerable gaps over South America, Australia, and parts of Central Asia (see Plates 2.1j,k for spatial coverage).
in western portions of South America, and in the Sonoran Desert of northwestern Mexico and the southwestern United States.

Figure 2.28 presents 2024 indices by their decile relative to the historical record. Southeast Asia, eastern China, southern Japan, India, and northern Australia stood out as regions where $T_w$X25 and $T_w$X27 exceedances were “very unusually frequent” (top decile; Figs. 2.28a,b). This was similar to 2023, but 2024 also had more widespread “very unusually frequent” exceedances for the threshold of $T_w$X29 (Fig. 2.28c)—notably over eastern China and southern Japan, reflecting a record-setting high-humid-heat event in July and early August. Counting only gridboxes where exceedances of the respective thresholds occurred climatologically (≥15 years within the 1991–2020 period), 24%–37% of the 2024 gridbox-level high-humid-heat frequency fell in the “very unusually frequent” category and just 0%–4% in the “very unusually infrequent” category (not shown).

High humid heat is of particular concern to human health (Xu et al. 2025), including its daily maximum values (Matthews et al. 2025) and nighttime values (Okamoto-Mizuno et al. 1999). This year, four new indices are introduced (Table 2.7). The minimum humid-heat intensity ($T_w$N) and high daily minimum humid-heat frequency ($T_w$N90p) are presented. Low humid-heat (i.e., fresh-cool) day frequencies ($T_w$X10p, $T_w$N10p) are also introduced, defined in Table 2.7 (Figs. 2.27a–c). Daily minimum wet-bulb temperatures are not always representative of nighttime wet-bulb temperatures, and so that distinction is avoided here.

The time series of $T_w$N indices closely follow the $T_w$X equivalents. $T_w$N in 2024 was well above average (+0.7°C) and only a fraction of a degree cooler than in 2023. $T_w$N90p was 34.4 days above average in 2024, breaking the record set in 2023 (23.8 days). Overall, 1973–2024 trends in maximum and minimum humid-heat intensity ($T_w$X = 0.13±0.03 decade⁻¹; $T_w$N = 0.18±0.03°C decade⁻¹) and

<figure><img src="image_73.png" /><figcaption>Fig. 2.28. High humid-heat extremes of 2024 as deciles over the period 1973–2024, ranking the number of days with maximum wet-bulb temperature ($T_w$)≥(a) 25°C ($T_w$X25), (b) 27°C ($T_w$X27), (c) 29°C ($T_w$X29), and (d) 31°C ($T_w$X31). Gridboxes bounded in pink indicate <15 years within the 1991–2020 period when exceedances occur. These panels are annotated with the percentage of observed gridbox area where an exceedance occurred in 2024 and climatologically (including only gridboxes with ≥15 years of at least one exceedance between 1991 and 2020). Data have been screened to remove gridboxes where temporal completeness is less than 70% (<36 years in 52), with whole years removed if one or more months are missing. White gridboxes (over land) represent regions with insufficient data.</figcaption></figure>
high humid-heat frequency ($T_w$X90p = 5.36±1.73 days year⁻¹ decade⁻¹; $T_w$N90p = 5.39±1.50 days year⁻¹ decade⁻¹) were similar, robustly portraying increasing humid heat. Positive trends for daily minimum humid-heat anomalies for $T_w$N and $T_w$N90p (Appendix Figs. A2.10, A2.11) were marginally more ubiquitous than for $T_w$X and $T_w$X90p (Plates 2.1j,k).

As for the equivalent dry-bulb temperature extremes (section 2b4; Figs 2.7b,d), frequencies of low humid-heat days (Fig. 2.27c) have decreased substantially, with the rate being slightly stronger than for high humid-heat days, but with the opposite sign. $T_w$X10p and $T_w$N10p were 19 days and 18.4 days below the 1991–2020 average in 2024, respectively, both of which were new record lows.

<table><caption>Table 2.7. Definitions of 10 humid-heat indices and their respective recent global land annual anomalies (1991–2020 base period). The global annual anomalies for the exceedance indices (not $T_w$X or $T_w$N) are the sum of the monthly spatial means over the globe. For $T_w$X and $T_w$N, the median is used as a more robust measure in the presence of outliers, finding the median first over space for each month and then over time.</caption><thead><tr><th>Index</th><th>Description</th><th>Meaning</th><th>2021 Global Anomaly</th><th>2022 Global Anomaly</th><th>2023 Global Anomaly</th><th>2024 Global Anomaly</th></tr></thead><tbody><tr><td>$T_w$X</td><td>Annual median of monthly maximum of the daily-maximum wet-bulb temperature</td><td>Intensity of maximum humid heat</td><td>0.2°C</td><td>0.1°C</td><td>0.6°C</td><td>0.5°C</td></tr><tr><td>$T_w$N</td><td>Annual median of monthly minimum of the daily-minimum wet-bulb temperature</td><td>Intensity of minimum humid heat</td><td>0.3°C</td><td>0.2°C</td><td>0.7°C</td><td>0.7°C</td></tr><tr><td>$T_w$X90p</td><td>Days per year exceeding the 90th percentile of the climatological daily-maximum wet-bulb temperature (seasonally varying)</td><td>Frequency of high daily-maximum humid-heat days relative to local climatology</td><td>8.4 days</td><td>7.5 days</td><td>26.1 days</td><td>35.6 days</td></tr><tr><td>$T_w$X10p</td><td>Days per year below the 10th percentile of the climatological daily-maximum wet-bulb temperature (seasonally varying)</td><td>Frequency of low daily-maximum humid-heat days relative to local climatology</td><td>-8.1 days</td><td>-6.8 days</td><td>-14.8 days</td><td>-19 days</td></tr><tr><td>$T_w$N90p</td><td>Days per year exceeding the 90th percentile of the climatological daily-minimum wet-bulb temperature (seasonally varying)</td><td>Frequency of high daily-minimum humid-heat days relative to local climatology</td><td>6.4 days</td><td>6.7 days</td><td>23.8 days</td><td>34.4 days</td></tr><tr><td>$T_w$N10p</td><td>Days per year below the 10th percentile of the climatological daily-minimum wet-bulb temperature (seasonally varying)</td><td>Frequency of low daily-minimum humid-heat days relative to local climatology</td><td>-6.2 days</td><td>-5.9 days</td><td>-12.6 days</td><td>-18.4 days</td></tr><tr><td>$T_w$X25</td><td>Days per year where the daily-maximum wet-bulb temperature was ≥25°C</td><td>Frequency of moderately high humid-heat days</td><td>3.6 days</td><td>4.1 days</td><td>6.1 days</td><td>11.0 days</td></tr><tr><td>$T_w$X27</td><td>Days per year where the daily-maximum wet-bulb temperature was ≥27°C</td><td>Frequency of high humid-heat days</td><td>3.6 days</td><td>3.5 days</td><td>9.3 days</td><td>13.8 days</td></tr><tr><td>$T_w$X29</td><td>Days per year where the daily-maximum wet-bulb temperature was ≥29°C</td><td>Frequency of very high humid-heat days</td><td>0.4 days</td><td>0.5 days</td><td>1.4 days</td><td>1.7 days</td></tr><tr><td>$T_w$X31</td><td>Days per year where the daily-maximum wet-bulb temperature was ≥31°C</td><td>Frequency of severe humid-heat days</td><td>0.1 days</td><td>-0.0 days</td><td>0.2 days</td><td>0.2 days</td></tr></tbody></table>
### 3. TOTAL COLUMN WATER VAPOR

—O. Bock, C. A. Mears, S. P. Ho, and X. Shao

In 2024, the global (60°S–60°N) mean total column water vapor (TCWV) was approximately 5% above the 1991–2020 climatological average (Table 2.8), with little difference between ocean and land, according to three global reanalyses (ERA5, MERRA-2, and JRA-3Q) and three observational datasets (Microwave Radiometer [MWR], satellite Global Navigation Satellite System Radio Occultation [GNSS-RO], and ground-based GNSS). It was the wettest year on record across all six datasets and for all three domains (global, ocean, and land), surpassing 2023, which had already set records in some datasets (Fig. 2.29). This remarkable positive anomaly is associated with the unprecedented high global-mean surface temperature (GMST; section 2b1), making 2024 the warmest year in a multi-dataset record dating back to the mid-1800s. The strong correlation between temperature and TCWV anomalies again highlights how tightly the Clausius–Clapeyron relation constrains the global climate system (O’Gorman and Muller 2010). The relation predicts that a GMST anomaly of 0.7°C (actual range was 0.63°C–0.72°C; section 2b1) corresponds to a TCWV anomaly of 4.9%, assuming a scaling factor of 7% per °C. Interestingly, there was a contrast between land and ocean GMST anomalies in 2024, with land slightly warmer than the ocean. This is also reflected in units of kg m⁻² of TCWV, with ocean TCWV anomalies greater than for land, but not in percentage (Table 2.8). This result suggests a substantial moisture transport from ocean to land on interannual timescales (Trenberth and Fasullo 2013).

The spatial distribution of TCWV anomalies in 2024 differs markedly from that of 2023, which exhibited a strong El Niño pattern, as well as from the La Niña years of 2021 and 2022 (Bock et al. 2024; Mears et al. 2023). In 2024, the moisture excess was nearly ubiquitous (Plate 2.1). Almost 90% of the global atmosphere was wetter than the 1991–2020 climatological mean, with approximately 65% of the increase occurring over the oceans and 25% over land. Some regions experienced extreme positive TCWV anomalies reaching 15%–20%, including northeastern Canada, Europe, the Middle East, eastern Asia, and northeastern Australia. Most of these regions are adjacent to oceans that recorded exceptionally high temperature anomalies in 2024, notably the Indian and Atlantic Oceans, polar seas, and the extratropical Pacific.

Figure 2.30a shows the year of record-high TCWV anomaly across the globe from 1988 to 2024 for the JRA-3Q reanalysis, which is consistent with other datasets. Strong moistening (and warming) has particularly been observed in recent years, especially in 2023 and 2024, over the Indian and Atlantic Oceans and most land areas in the Northern Hemisphere. In contrast, the central-eastern Pacific experienced its strongest moist anomaly in 1998 during the exceptional 1997/98 El Niño event. The lower tropospheric temperature set a record in 2024 across most of the tropics, including the tropical Pacific as a whole (Plate 2.1f). Figure 2.30b shows that more than 20% of the globe recorded its highest TCWV anomaly in 2024—far exceeding 2023 (which ranks

<figure><img src="image_74.png" /><figcaption>Fig. 2.29. Global mean (60°S–60°N) total column water vapor annual anomalies (%; 1991–2020 base period) over (a) land and ocean, (b) ocean-only, and (c) land-only from observations and reanalyses. The shorter time series from the observations have been adjusted, so there is zero mean difference relative to the ERA5 results during their respective periods of record.</figcaption></figure>
<table><caption>Table 2.8. Global mean (60°S–60°N) total column water vapor (TCWV) anomalies (kg m⁻² (%); 1991–2020 base period) for 2024 and linear trends (kg m⁻² decade⁻¹) over the period 1991–2024 for reanalyses and Microwave Radiometer (MWR), (*) 2006–24 for Global Navigation Satellite System Radio Occultation (GNSS-RO), (**) 1995–2024 for ground-based GNSS (including 166 stations over land). Note that the inconsistency (ocean anomaly smaller than land) between GNSS-RO anomalies and those from reanalyses and satellites is likely due to the shorter base period.</caption><thead><tr><th colspan="7">TCWV Anomalies in 2024, Units in kg m<sup>-2</sup> (%)</th></tr><tr><th>Dataset</th><th>ERA5</th><th>MERRA-2</th><th>JRA-3Q</th><th>MWR</th><th>GNSS-RO*</th><th>GNSS**</th></tr></thead><tbody><tr><td>Global</td><td>1.32 (4.9%)</td><td>1.50 (5.4%)</td><td>1.50 (5.5%)</td><td></td><td>1.45 (5.5%)</td><td></td></tr><tr><td>Ocean</td><td>1.40 (4.9%)</td><td>1.38 (4.7%)</td><td>1.59 (5.5%)</td><td>1.63 (5.5%)</td><td>1.28 (4.6%)</td><td></td></tr><tr><td>Land</td><td>1.04 (4.5%)</td><td>1.26 (5.2%)</td><td>1.19 (5.4%)</td><td></td><td>1.49 (6.6%)</td><td>1.48 (7.8%)</td></tr></tbody></table><table><thead><tr><th colspan="7">Trends over 1991–2024, Units in kg m<sup>-2</sup> (% decade<sup>-1</sup>)</th></tr><tr><th>Dataset</th><th>ERA5</th><th>MERRA-2</th><th>JRA-3Q</th><th>MWR</th><th>GNSS-RO*</th><th>GNSS**</th></tr></thead><tbody><tr><td>Global</td><td>0.42 ± 0.06<br>(1.55 ± 0.23)</td><td>0.41 ± 0.07<br>(1.48 ± 0.25)</td><td>0.37 ± 0.10<br>(1.37 ± 0.35)</td><td></td><td>0.57 ± 0.17<br>(2.15 ± 0.63)</td><td></td></tr><tr><td>Ocean</td><td>0.49 ± 0.06<br>(1.71 ± 0.22)</td><td>0.44 ± 0.07<br>(1.49 ± 0.24)</td><td>0.36 ± 0.12<br>(1.25 ± 0.40)</td><td>0.48 ± 0.07<br>(1.64 ± 0.25)</td><td>0.53 ± 0.16<br>(1.89 ± 0.58)</td><td></td></tr><tr><td>Land</td><td>0.23 ± 0.07<br>(0.98 ± 0.29)</td><td>0.34 ± 0.08<br>(1.45 ± 0.35)</td><td>0.43 ± 0.06<br>(1.90 ± 0.28)</td><td></td><td>0.53 ± 0.20<br>(2.38 ± 0.90)</td><td>0.39 ± 0.08<br>(2.03 ± 0.47)</td></tr></tbody></table>

second at ~10%) and all previous years, including the three strongest El Niño years within the period (1997/98, 2009/10, and 2015/16).

The pronounced wet anomaly in 2024 significantly impacts the linear trend estimated from 1991 onward. The global-mean linear trend in ERA5 increases from 0.38 kg m⁻² to 0.42 kg m⁻² decade⁻¹ (1.40% decade⁻¹ to 1.55% decade⁻¹) between 2023 (Bock et al. 2024) and 2024 (Table 2.8). This increase is observed over ocean and land and is consistent across all datasets. Over the 34-year period, the total atmospheric water vapor content has increased by nearly 1.4 kg m⁻² (or 5.1%), assuming an average trend value of 0.4 kg m⁻² decade⁻¹ (1.5% decade⁻¹).

This assessment is based on observations from satellite-borne MWRs over the oceans (Remote Sensing Systems [RSS] satellite; Mears et al. 2018), GNSS-RO data from the Constellation Observing System for Meteorology, Ionosphere, and Climate (COSMIC), Meteorological Operational satellite (MetOp)-A, -B, -C, COSMIC-2, PlanetIQ, Korea Multi-Purpose Satellite-5 (KOMPSAT-5), PAZ, TerraSAR-X (TSX), TerraSAR-X add-on for Digital Elevation Measurement (TDX), and Spire missions (Ho et al. 2020; Shao et al.

<figure><img src="image_75.png" /></figure>

<figure><img src="image_76.png" /><figcaption>Fig. 2.30. (a) Record years in annual total column water vapor anomalies for the Japanese Reanalysis for Three Quarters of a Century (JRA-3Q) from 1988 to 2024. (b) Fraction of the globe for each year in (a).</figcaption></figure>
2023), covering both land and ocean, as well as ground-based GNSS observations over land and islands (Bock 2025). Three global reanalysis products were used: ERA5 (Hersbach et al. 2020), MERRA-2 (Gelaro et al. 2017), and JRA-3Q (Kosaka et al. 2024). All three reanalyses assimilate satellite microwave radiometer data (as radiances) and GNSS-RO data (as bending angles) but not ground-based GNSS measurements, which serve as an independent validation dataset.

## 4. UPPER-TROPOSPHERIC HUMIDITY

—V. O. John, L. Shi, E.-S. Chung, T. Stevens, R. P. Allan, S. A. Buehler, and B. J. Soden

The global-mean upper-tropospheric humidity (UTH; %rh) anomalies, shown using relative humidity in Plate 2.1m (based on microwave data) and Fig. 2.31a, were slightly below normal in 2024, especially during the first half of the year (note that %rh is the unit for relative humidity, which is a percentage of how saturated the air is). This is expected during El Niño, which ended in boreal spring 2024, associated with large drier-than-average relative humidity anomalies at tropical and subtropical latitudes over the Pacific Ocean (McCarthy and Toumi 2004). As shown in Plate 2.1m, an annual average anomaly map of UTH in 2024, these drier areas are almost balanced by more humid-than-average anomalies in other areas of the tropics, unlike in 2023 (John et al. 2024). In 2023, there were slightly more areas in the tropics that were more humid than average (central and eastern Pacific, tropical Atlantic, and central Africa), but in 2024 the only substantial areas with above-average humidity were east Africa, the Arabian Sea, central India, northeast Australia, and adjacent areas of the western Pacific.

UTH anomalies, in general, reflect the large-scale circulation patterns. A strong positive phase of the Indian Ocean dipole (IOD) can be seen. Here, the cooler-than-normal eastern Indian Ocean and warmer-than-normal western Indian Ocean led to reduced convection in the east

and enhanced convection in the west. There were generally dry conditions over northern South America and moist signatures over central India and the Horn of Africa. Very dry patches over southern Africa indicate ongoing drought in those regions, which began in late 2023. Despite 2023 and 2024 both being dominated by El Niño and a positive IOD, the spatial patterns are different. There were more widespread negative anomalies over North and South America and the western tropical Pacific in 2024 compared to 2023, as well as over eastern Asia and western Australia.

The mean and standard deviation (1-sigma) of the global monthly anomaly time series (Fig. 2.31) in 2024 were –0.10±0.22 %rh for the microwave based data (Chung et al. 2013), –0.47±0.44 %rh for the infrared based data (Shi and Bates 2011), and –0.31±0.44 %rh for ERA5 data (Hersbach et al. 2020). There is no significant long-term trend in any of the datasets. This is in line with the theoretical consideration that the large-scale relative

humidity in the upper troposphere remains roughly unchanged (Ingram 2010). However, the absolute humidity (amount of water vapor) in the upper troposphere has increased. This is illustrated in Fig. 2.31b, which shows the difference between mid-to-upper-tropospheric mean layer temperature (Microwave Sounding Unit [MSU] T2; Zou et al. 2023) and the measured brightness temperature of the 6-µm water vapor channel (High-resolution Infrared Radiation Sounder [HIRS] T12), which is sensitive to the upper-tropospheric relative humidity. As the amount of water vapor in the upper troposphere (UT) increased, the emission level of the water vapor channel shifted higher in the troposphere. This resulted in water vapor emissions being associated with a lower temperature. Therefore, the positive trend in the difference (T2 – T12) time

<figure><img src="image_77.png" /><figcaption>Fig. 2.31. Time series of (a) global monthly-mean anomaly upper-tropospheric humidity (UTH) for the three datasets (%rh; see text for details) and (b) the difference between upper-tropospheric temperature (T2) and water vapor channel (T12) brightness temperatures (K). Anomalies are with respect to the 2001–20 base period.</figcaption></figure>
series indicates moistening of the upper troposphere (Chung et al. 2014; Simmons 2022; John et al. 2024). The differences (T2 – T12) in 2024 were the largest in the series, implying record-high UT absolute humidity in 2024. These strikingly large anomalies are consistent with the presence of El Niño (for a portion of the year) as well as the record-high surface and lower tropospheric temperatures, near-surface specific humidity, and TCWV. The monthly anomalies became substantially less extreme late in 2024 as El Niño dissipated.

## 5. PRECIPITATION

—M. Ziese, R. S. Vose, R. Adler, G. Gu, and X. Yin

Precipitation is the primary source of fresh water needed for drinking, agriculture, hydropower, human wellbeing, and many other purposes. Both a lack (drought) and excess (e.g., flood) of water can have a large impact on human activities. The analyses presented here are based on data from two datasets: in situ from the Global Precipitation Climatology Centre (GPCC; Becker et al. 2013) and gauge-adjusted (including GPCC) satellite data from GPCP Version 3.2 (Huffman et al. 2023).

In 2024, excess precipitation (relative to the 1991–2020 baseline) was observed across much of the tropics (land and ocean; Fig. 2.32), Asia, and the northwestern Pacific, as well as the northern and southern subtropical Atlantic. A precipitation deficit occurred over southern Africa, the southeast Indian Ocean, the subtropical Pacific, South America, and the North Atlantic.

Globally, 2024 was the third-wettest year since 1983 (Fig. 2.33c; GPCP dataset only). While global land precipitation was around normal, precipitation over the oceans was far above normal, only exceeded by that of 1998, 2015, and 2016. The unusual high precipitation totals over the oceans (shown across the seasons in Fig. 2.32 but not in the land-only Plate 2.1n), were likely associated with above-normal sea surface temperatures (Plate 2.1a), which produced more regional evaporation (Plates 2.1h,1) and water for rainfall. This can be seen, for example, over the Gulf of America/Gulf of Mexico, Caribbean, and adjacent western Atlantic (Fig. 2.32) as well as over the Indian Ocean, western Pacific and South Pacific Convergence Zone, and northwestern Pacific. High ocean precipitation could be associated with the 2023/24 El Niño event; previous large El Niño events of 1997/98 and 2015/16 also preceded precipitation excess over the oceans.

Spatial variability of precipitation is higher compared to other atmospheric parameters such as air temperature. Therefore, precipitation totals as well as anomalies show a patchy pattern, where

<figure><img src="image_78.png" /><figcaption>Fig. 2.32. 2024 annual and seasonal precipitation anomalies (mm day⁻¹; 1991–2020 base period) for (a) annual, (b) Jan–Mar (JFM), (c) Apr–Jun (AMJ), (d) Jul–Sep (JAS), and (e) Oct–Dec (OND). (Data source: Global Precipitation Climatology Project [GPCP].)</figcaption></figure>
regions with excess precipitation (above the long-term mean) can be close to those with a pre-
cipitation deficit (below the long-term mean).

Focusing on land regions (Plate 2.1n), excess rainfall was observed in many parts of central and eastern Africa as well as in the Sahel region. Lower-than-usual rainfall was measured in southern Africa and western central Africa, reflecting drier-than-usual wet seasons. Madagascar and northwest Africa were also drier, having experienced multi-year drought (since 2020 and 2019, respectively; Fig. 2.32). These correspond with negative soil moisture anomalies (section 2d11).

Conditions were drier than usual in the Hindu Kush, parts of Southeast Asia, the Philippines, around the Laptev Sea in the Arctic, and also around the Himalayas. The latter was associated with a dry early monsoon season. Above-normal annual precipitation was mainly observed in the east, southeast, southwest, and northwest of Asia. All seasons were wetter than usual in northwestern and eastern Asia. Southern Asia exhibited strong wet anomalies in July, August, and September, but was otherwise mostly drier than usual.

In the Amazon basin, the drought that began in 2023 continued in 2024; all seasons were drier than usual (Fig. 2.33). While the majority of South America was drier than usual, some spots in the north, southeast, and south, such as western Patagonia, received excess precipitation (see section 7d for details).

Excess precipitation occurred across North America from the central Rocky Mountains to Florida (Plate 2.1n). Also, southern Central America and parts of the Caribbean, as well as some spots in north and northwest North America, were wetter than normal. Northern Central America, the northern Caribbean, and northeast and northern central North America were drier than usual.

The coastal regions in southern (including Tasmania), northwestern, and northeastern Australia had below-normal precipitation, as did smaller islands in the Pacific Ocean. The Maritime Continent, parts of northern, eastern, and western Australia, and some spots in New Zealand were wetter than normal.

Western and Central Europe as well as Scandinavia received more precipitation than the long-term mean. Eastern and southeastern Europe, the Middle East, and the region northward of the Black Sea were drier than normal.

The state of ENSO (El Niño–Southern Oscillation) influences regional precipitation patterns. El Niño conditions were present at the beginning of the year, then decreased and reached neutral conditions by April–June, which prevailed until the end of the year when weak La Niña-like conditions emerged.

Associated with the El Niño conditions in the first months of the year are the below-normal precipitation totals in southern Africa and above-normal totals in eastern Africa, along with the wet conditions in East Asia, in southwestern North America, and northward of the Gulf of America/Gulf of Mexico (Fig. 2.32). These conditions dissipated by the latter half of 2024. Due to the ENSO-neutral conditions later in the year, corresponding seasonal anomalies were attributed to regional circulation and year-to-year variability.

<figure><img src="image_79.png" /><figcaption>Fig. 2.33. 2024 globally averaged annual precipitation anomalies (mm yr⁻¹) relative to the 1991–2020 baseline period for (a) land areas, (b) ocean areas, and (c) globally (land and ocean).</figcaption></figure>
# 6. LAND SURFACE PRECIPITATION EXTREMES

—M. R. Tye, S. Blenkinsop, M. G. Bosilovich, I. Durre, C. Lennard, I. Pinto, A. J. Simmons, and M. Ziese

Globally, 2024 was the wettest year on record with respect to extreme precipitation. The global-mean annual maximum daily precipitation total (Rx1day) from station and reanalysis records surpassed the previous record years of 2010 and 2020 (Fig. 2.34a).

More than two-thirds of the globe experienced unusually high precipitation (section 2d5), with attendant record-breaking extremes mostly occurring within the tropical belt. Many extremes occurred anomalously within extended droughts (sections 2d11, 2d12) or were seasonally unusual (e.g., during a dry season or during winter).

The strong El Niño in the first quarter, as well as the La Niña-like pattern that it transitioned to in the last quarter, both supported intensified precipitation extremes due to the accumulated atmospheric moisture associated with higher temperatures. Coupled with record ocean temperatures (see sections 2b1, 3b; Cheng et al. 2025), these conditions drove abnormally intense and clustered typhoons in the western Pacific (Cassidy 2024) and increased the distance of inland moisture transport into China (WWA 2024a). Cut-off lows across the North Atlantic spurred increased convective activity, which resulted in several record short-duration (sub-daily) rainfall totals. This activity included Storm Boris, which affected a large area in central Europe in September (see Sidebar 7.2; Magnusson et al. 2025), and intense rainfall that resulted in catastrophic flooding in Valencia, Spain, in October (see section 7f4; Pucik 2024). ENSO-associated shifts in the Intertropical Convergence Zone also impacted the locations of monsoonal systems and contributed to longer-duration precipitation extremes. A more-intense-than-normal hydrological cycle was apparent through connected mechanisms: above-normal air temperatures (section 2b1) and SSTs (sections 2b1, 3b), above-average seasonal precipitation (section 2d5), high soil moisture anomalies (section 2d11), high evaporation over land (section 2d13), high water vapor content (sections 2d1, 2d3), and the resultant precipitation maxima.

Plate 2.10 and Figs. 2.34b, 2.35 show similar regions of wetter-than-normal one-day/accumulated five-day maxima (Rx1day, Rx5day) in a band between 30°S and 30°N. Higher latitudes experienced pockets of abnormal extreme precipitation within larger areas of drought conditions. Record-breaking accumulations, where noted, are limited by the availability of reliable long-duration observations.

At the continental scale, Australia experienced one of its wettest years on record (see section 7h4) with accompanying wetter-than-average precipitation maxima over the Northern Territory. Following record-breaking Rx1day/Rx5day in January, the transition to La Niña-like conditions

<figure><img src="image_80.png" /><figcaption>Fig. 2.34. (a) Global mean anomaly (with respect to 1991–2020) of one-day maxima (Rx1day) over land from the Met Office Hadley Centre Extremes dataset version 3 (HadEX3; Dunn et al. 2020), Global Historical Climatology Network Daily Extremes dataset (GHCNDEX; Donat et al. 2013), ERA5 (Hersbach et al. 2020), and MERRA-2 (Gelaro et al. 2017). (b) Global Rx1day anomalies in 2024 with respect to the 1991–2020 mean from Multi-Source Weighted-Ensemble Precipitation (MSWEP; Beck et al. 2019) highlighting a band of wet anomalies across the tropics.</figcaption></figure>
later in the year resulted in less severe precipi-
tation extremes over Australasia compared
with the rest of the globe (WMO 2024).

Despite fewer-than-normal typhoons
in the western North Pacific (see section
4g4 for details), the season brought excep-
tionally heavy Rx1day/Rx5day and induced
extensive flooding across the Philippines and
surrounding countries in Southeast Asia in July
and September (Tandon 2024). An exceptional
clustering of six active typhoons occurred in
the basin in less than a month in late October
and November, further compounding the
impacts of previous extreme precipitation
(Cassidy 2024). As part of an active North
Atlantic hurricane season (see section 4g2),
a series of tropical cyclones developed in

September and October. Notably, Hurricanes Helene and Milton each rapidly intensified, with Rx5day totals doubling previous records over parts of the eastern United States.

Precipitation extremes in southwestern Asia arose from the wetter-than-average monsoon and pre-monsoon (April–June), as the Indian Ocean experienced a below-average year for cyclones (see sections 4f, 4g5). Widespread heavy precipitation and flash floods over Pakistan and Afghanistan in April (Pakistan Meteorological Department 2025) were succeeded by record-breaking sub-daily precipitation in Lahore, Pakistan, in August (DW 2024). The highest multi-day precipitation in over 50 years occurred over northeast India and Bangladesh in August (Pandey and Sengupta 2024; Kamal et al. 2024), and Nepal in September (WWA 2024b).

A series of unusual Rx1day/Rx5day values occurred in otherwise arid or drought-affected countries surrounding the Mediterranean, the Persian Gulf, northeastern Africa, and the Sahel (section 2d5), all connected to active convection systems from cut-off lows. These included: the heaviest Rx1day in 75 years over northern Oman and the United Arab Emirates in April (Zhang et al. 2025); a series of heavy events that together generated the highest July rainfall total (since records began in 1956) at Cape Town International Airport in South Africa, triple its monthly climatology (South African Weather Service 2024); Rx1day and Rx5day each exceeding the monthly climatology in Morocco, Niger, and Nigeria in September; and historic hourly precipitation in southeastern Spain in late October (Kothari 2024). Torrential rain also occurred over Kenya and Tanzania in April, South Africa in June, and central Africa throughout July and August (NOAA NCEI 2025a).

Europe was affected by heavy precipitation throughout the year (ECMWF 2025). Geographic clusters of record-breaking Rx1day and Rx5day include France in April, Germany and the Netherlands in May and June, Poland and Czechia in September, and the United Kingdom and Ireland in November (see section 7f for details).

While much of Central and South America were in extended drought, isolated intense pre-
cipitation occurred across southwestern Brazil, Uruguay, and Argentina in March. Rio Grande
do Sul experienced record-breaking Rx1day in April and May (see Sidebar 7.1; Zhang et al. 2025).
Near-record precipitation also fell over Colombia and Bolivia during November (NOAA NCEI
2024; see section 7d).

7. CLOUDINESS

— C. Phillips and M. Foster

Cloud area fraction increased in 2024 compared to the record low observed in 2023. According to PATMOS-x observations (Foster et al. 2023; Fig. 2.36), global-mean cloud area fraction was significantly higher (0.4%) than in 2023. This is consistent with the Clouds and the Earth's Radiant Energy System (CERES) EBAF-TOA Ed4.2.1 cloud radiative effect data (Loeb et al. 2018). Compared to 2023, the global-mean shortwave cloud radiative effect (SWCRE) was 0.66 W m⁻² more

<figure><img src="image_81.png" /><figcaption>Fig. 2.35. 2024 accumulated one-day maxima (Rx1day; mm) anomalies from 1991–2020 from ERA5.</figcaption></figure>
“reflective”, and in the longwave, global-mean cloud radiative effect (LWCRE) was 0.07 W m⁻² more “insulating” in 2024. Combining shortwave and longwave changes (−0.59 W m⁻²), the global-mean net cloud radiative effect corrected from the large 2023 anomaly such that 2024 was only 0.02 W m⁻² above the 2000–20 average (−17.81 W m⁻²).

Plate 2.1p shows the annual anomalies of cloud area fraction in 2024 compared to the 1991–2020 average. In 2023, the tropical Indian Ocean had an extremely low cloud area fraction, but it returned to normal levels in 2024. Southern Africa and South America saw below-average cloud area fraction, which aligns with observations of below-average water vapor and precipitation in these regions (Plates 2.1l,n).

Although cloud area fraction increased from 2023 to 2024, it remained well below the long-term average (−1.49% relative to 1991–2020) and was the second lowest since records began in 1980 (Fig. 2.36). Similarly, although greater than 2023, the CERES shortwave cloud radiative effect was both less reflective (SWCRE: −0.57 W m⁻²) and less insulating (LWCRE: −0.55 W m⁻²) than the long-term 2000–20 average. These findings of below-average cloud area fraction are consistent with the theory of a long-term trend of decreasing cloud cover discussed in previous reports (Phillips and Foster 2024). For example, global-mean shortwave cloud radiative effect has been trending towards less “reflective” (−0.44 W m⁻² decade⁻¹), and the global-mean longwave cloud radiative effect has been trending towards less “insulating” since 2000 (−0.39 W m⁻² decade⁻¹).

As an experiment, a GEO-Ring composite of Geostationary Operational Environmental Satellites (GOES)—namely GOES-16 and GOES-18—along with Himawari-9, Meteosat-9, and Meteosat-10, was processed for every six hours in 2024. Figure 2.37a shows the diurnal range of annual average cloud area fraction for 2024, in other words the difference in cloud between the peak and the trough time of day. The diurnal range of cloud area fraction for any given location is low, with an average value of 7.8%. Figure 2.37b shows the diurnal cycle of global-mean cloud area fraction for 2024. This is even smaller because some regions cancel each other out when the global mean is calculated. However, there are some regions with much higher diurnal variability. Most salient are the regions of stratocumulus clouds off the west coasts of continents. These

<figure><img src="image_82.png" /><figcaption>Fig. 2.36. Global annual mean cloud area fraction (%) from PATMOS-x v6.0.</figcaption></figure>

<figure><img src="image_83.png" /><figcaption>Fig. 2.37. The annual mean (a) diurnal range over the globe and (b) annual global mean diurnal cycle of average cloud area fraction (%) for 2024, as observed by the GEO-Ring composite of Geostationary Operational Environmental Satellite (GOES)-16, GOES-18, Himawari-9, Meteosat-9, and Meteosat-10.</figcaption></figure>
regions should be considered carefully when performing cloud climatological studies, as the
timing of observation can have a large impact on the results. The PATMOS-x observations used
here (Plate 2.1p; Fig. 2.36) come from satellites with drifting observation times and must be cor-
rected for such diurnal effects, though corrections to the 2024 global mean are negligible.
Between 2023 and 2024, the NOAA-18 satellite drifted from 10:31 to 10:41 local time for morning
equatorial crossing, NOAA-19 drifted from 08:37 to 09:17, and NOAA-15 drifted from 07:28 to
07:21 local time. Drift in the morning overpass is mostly cancelled by drift in the afternoon
overpass such that the expected ensemble-mean effect is -0.03%, much smaller than the
observed increase in cloud area fraction (0.4%).
In 2024, water storage and levels across 4487 lakes exhibited slight overall increases compared to a 1993–2020 baseline period based on two global datasets. Lake storage analysis was based on the GloLakes dataset (Hou et al. 2024; 4190 lakes, median area 5.52 km²), and lake level was analyzed using the Global Reservoirs and Lakes Monitor (GREALM) dataset (Birkett et al. 2011; 297 lakes, median area 449.2 km²). Relative to the 1993–2020 baseline, median storage increased by 1.61% in 2024, representing a median rise of 0.295 million cubic meters (MCM). The median lake level increased by 0.12 m, with anomalies ranging between –53 m and +28.9 m. However, marginal global changes obscured more substantial regional changes. After combining both datasets, level or storage increased in 57.8% and decreased in 42.2% of lakes relative to the baseline. A Welch's t-test comparing level and storage observations in 2024 relative to the baseline identified some of these trends as statistically robust (25.6% increased and 16.5% decreased; *p*<0.05). Long-term trends from these two datasets sometimes diverged (Fig. 2.38) and may not be representative of all lakes globally.

Regional patterns in lake storage were observed. Countries with the largest mean increases relative to the baseline included Syria, Senegal, Belize, Cambodia, Angola, Bangladesh, Sudan, and Libya (+23.4% to +84%), while decreases were most prominent in Niger, Chad, Mongolia, Algeria, Namibia, Argentina, Botswana, and Bosnia and Herzegovina (–20% to –74.8%). For lakes with substantial storage anomalies (>±20%), a weak positive correlation (*r*² = 0.11) between increased storage and higher ERA5 precipitation in 2024 was found, suggesting partial climatic influence (Hersbach et al. 2020). However, it is not advisable to over-interpret these country-level trends given the limited and uneven sampling of lakes, which may not capture broader hydrologic dynamics.

Storage anomaly variance across binned lake sizes and between lakes classified as ‘natural’ or ‘reservoir’ was also analyzed using the Global Reservoir and Dam Database (Lehner et al. 2011). Statistically robust differences in storage variance were found across area bins and classifications (*p*<0.05; Levene’s test for homogeneity of variance; Levene 1960), with smaller lakes and reservoirs showing higher anomaly variability (Fig. 2.39). The prevalence of both increasing and decreasing lake trends aligns with previous studies (Kraemer et al. 2020; Y. Feng et al. 2022). Discrepancies in global storage trends compared to more recent work (Yao et al. 2023) are likely a reflection of dataset differences. Continual monitoring of lake anomalies is critical for more accurately predicting dynamics in water availability, ecosystem resilience, and flood and drought risk (e.g., Weyhenmeyer et al. 2024; Han et al. 2024).

Both lake datasets incorporate remote sensing data to estimate storage (GloLakes) or level (GREALM). GloLakes combines Ice, Cloud, and land Elevation Satellite 2 (ICESat-2)

<figure><img src="image_84.png" /><figcaption>Fig. 2.38. Lake water storage and level anomalies relative to a baseline averaged period of 1993–2020 across each year from 1993 to 2024. Yearly median water storage (black) and level (gray) anomalies averaged across each water body are shown on dual y axes, expressed in million cubic meters (MCM) for lake water storage anomalies and meters (m) for lake water level anomalies. Local regression (loess) smoothing is applied to the annual median anomalies represented by the two curves.</figcaption></figure>

<figure><img src="image_85.png" /><figcaption>Fig. 2.39. Annual lake storage anomalies (%) for 2024 relative to 1993–2020 binned by lake size, and categorized as “natural” or “reservoir” based on inclusion in the Global Reservoir and Dam Database (Lehner et al. 2011). Lake bin counts (n) are displayed on top (black font) and reservoir counts are shown on bottom (gray font).</figcaption></figure>
laser altimetry (Jasinski et al. 2023), GREALM radar altimetry data, and optical imagery from Landsat and Sentinel-2. The GloLakes dataset was refined by selecting lakes with at least 20 years of data, no data gaps longer than three years, and at least three observations in 2024. These 4190 lakes represent just 0.89% of global lake volume (HydroLAKES; Messager et al. 2016). To improve volume coverage, GREALM lake level data (Birkett et al. 2011) is also incorporated, adding an additional 297 lakes covering 88.7% of HydroLAKES volume. Anomalies are reported relative to a 1993–2020 baseline, consistent with GREALM availability. Both datasets are limited in spatiotemporal coverage globally, with overrepresentation in North America (Plate 2.1q) and underrepresentation in small (<1 km²) lakes (Fig. 2.39), which dominate global lake area and storage variability (Pi et al. 2022; Xu et al. 2024). Further, satellite-based estimates of storage may not fully capture fine-scale temporal dynamics. Among the 85 lakes shared between GloLakes and GREALM, the median correlation coefficient (r²) across each lake between storage and level anomalies was 0.361, yet 82.3% of lakes agree on 2024 anomaly direction. For the lakes present in both datasets, only anomalies for GREALM are provided, given the denser interannual record. Future integration of data from the recently launched Surface Water and Ocean Topography (SWOT) satellite mission or data from longer missions such as the Moderate Resolution Imaging Spectroradiometer (MODIS) may help increase spatiotemporal coverage.

## 9. RIVER DISCHARGE AND RUNOFF

—J. Casado-Rodríguez, S. Grimaldi, and P. Salamon

From the perspective of river discharge and runoff, 2024 was another dry year, continuing a trend of four consecutive years of below-normal global runoff (Fig. 2.40) and six years with below-normal global river discharge (Fig. 2.41).

Globally, runoff in 2024 exhibited drier-than-usual conditions compared with the reference period 1991–2020 (Fig. 2.40). However, this anomaly was not as pronounced as in 2023, which remains the driest year in the time series. A shift towards normal conditions was observed, likely associated with the transition from El Niño at the beginning of the year to a neutral phase (Oceanic Niño Index in Fig. 2.40; see section 4b for details). Additionally, 2024 was characterized by an intense negative phase of the Pacific Decadal Oscillation (PDO), which was the most negative since 1980. Despite this overarching pattern, significant regional differences were observed (Plate 2.1r). The Amazon, La Plata, and Congo basins experienced an extremely dry year (Toreti et al. 2024c). The anomaly in the Congo is partially attributed to an erroneous negative trend in the ERA5 precipitation over this region (section 2d5; Lavers et al. 2022; Liu et al. 2024). Drought conditions also affected the Atlantic and Pacific coasts of North America,

<figure><img src="image_86.png" /><figcaption>Fig. 2.40. Interannual variability of global runoff (b) and its connections with the (a) Pacific Decadal Oscillation (PDO) and (c) Oceanic Niño index (ONI). In panel (b), the gray line represents the monthly time series of anomalies compared with the base period 1991–2020, the black line represents the 12-month moving average, and the shading indicates the phase of the two indices (PDO in the upper part and ONI in the lower part).</figcaption></figure>
southeastern Europe (Toreti et al. 2024b), Southeast Asia, and parts of Siberia. Conversely, Central America, Central and Northern Europe, the Indian subcontinent, the Pacific coast of Asia, and insular Southeast Asia experienced wetter-than-usual conditions.

A similar pattern was observed in freshwater discharge into the oceans, which remained below normal overall, though with notable regional variations (Fig. 2.41; Plate 2.1s). Discharge

<figure><img src="image_87.png" /><figcaption>**Fig. 2.41.** Interannual variability and seasonality of freshwater discharge to the (a) global, (b) Arctic, (c) Atlantic, (d) Indian, and (e) Pacific Ocean basins (km³ day⁻¹). In the left panels, the black line represents the annual discharge anomaly, and the background heat map shows the monthly anomalies with respect to the reference seasonality in the base period 1991–2020. The right panels exhibit the seasonality, where the black line represents the climatological mean, the gray shading indicates the interquartile range in the climatology, and the blue line shows the seasonal variation in 2024.</figcaption></figure>