Articles | Volume 26, issue 8
https://doi.org/10.5194/nhess-26-4071-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/nhess-26-4071-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Assessing financial risk to property portfolios from physical rainfall extremes
Laura C. Dawkins
CORRESPONDING AUTHOR
Met Office, FitzRoy Road, Exeter, EX1 3PB, United Kingdom
Freya K. Garry
Met Office, FitzRoy Road, Exeter, EX1 3PB, United Kingdom
Dan J. Bernie
Met Office, FitzRoy Road, Exeter, EX1 3PB, United Kingdom
University of Bristol Medical School, Beacon House, Queens Road, Bristol, BS8 1QU, United Kingdom
Jason A. Lowe
Met Office, FitzRoy Road, Exeter, EX1 3PB, United Kingdom
Priestley International Centre for Climate, University of Leeds, Leeds, United Kingdom
Theodoros Economou
Met Office, FitzRoy Road, Exeter, EX1 3PB, United Kingdom
College of Engineering, Mathematics and Physical Sciences, University of Exeter, Exeter, United Kingdom
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Paula L. M. Gonzalez, Duncan Ackerley, Eloise Matthews, Dan Suri, Emily Wallace, Matthew Wheeler, Jason A. Lowe, and Daisy Harley-Nyang
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2026-181, https://doi.org/10.5194/essd-2026-181, 2026
Revised manuscript accepted for ESSD
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The Named Storms Catalogue is the first dataset to systematically evaluate ten years of UK storm naming using carefully designed metrics developed with Met Office meteorologists. It reveals how storms evolved and the hazards they created, enabling unprecedented comparison, ranking, and insight. The work introduces the dataset, showcases three case studies, and explores current and future uses with a view to helping experts and responders better understand and manage storm impacts.
Detlef P. Van Vuuren, Brian C. O'Neill, Claudia Tebaldi, Benjamin M. Sanderson, Louise P. Chini, Pierre Friedlingstein, Tomoko Hasegawa, Keywan Riahi, Bala Govindasamy, Nico Bauer, Veronika Eyring, Cheikh M. N. Fall, Katja Frieler, Matthew J. Gidden, Laila K. Gohar, Annika Högner, Andrew D. Jones, Jarmo Kikstra, Andrew King, Reto Knutti, Elmar Kriegler, Peter Lawrence, Chris Lennard, Jason Lowe, Camilla Mathison, Shahbaz Mehmood, Zebedee Nicholls, Luciana F. Prado, Qiang Zhang, Steven K. Rose, Alex C. Ruane, Marit Sandstad, Carl-Friedrich Schleussner, Roland Seferian, Jana Sillmann, Chris Smith, Anna A. Sörensson, Swapna Panickal, Kaoru Tachiiri, Naomi Vaughan, Saritha S. Vishwanathan, Tokuta Yokohata, Marco Zecchetto, and Tilo Ziehn
Geosci. Model Dev., 19, 2627–2656, https://doi.org/10.5194/gmd-19-2627-2026, https://doi.org/10.5194/gmd-19-2627-2026, 2026
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We propose a set of seven plausible 21st century emission scenarios, and their multi-century extensions, that will be used by the international community of climate modeling centers to produce the next generation of climate projections. These projections will support climate, impact and mitigation researchers, provide information to practitioners to address future risks from climate change, and contribute to policymakers’ considerations of the trade-offs among various levels of mitigation.
Pantelis Georgiades, Theo Economou, Yiannis Proestos, Jose Araya, Jos Lelieveld, and Marco Neira
Earth Syst. Sci. Data, 17, 1153–1171, https://doi.org/10.5194/essd-17-1153-2025, https://doi.org/10.5194/essd-17-1153-2025, 2025
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Climate change is posing increasing challenges in the dairy cattle farming sector, as heat stress adversely affects the animals' health and milk production. To accurately assess these impacts, we developed a machine learning model to downscale daily climate data to hourly Temperature Humidity Index (THI) values. We utilized historical weather data to train our model and applied them to future climate projections, under two climate scenarios.
Benjamin M. Sanderson, Ben B. B. Booth, John Dunne, Veronika Eyring, Rosie A. Fisher, Pierre Friedlingstein, Matthew J. Gidden, Tomohiro Hajima, Chris D. Jones, Colin G. Jones, Andrew King, Charles D. Koven, David M. Lawrence, Jason Lowe, Nadine Mengis, Glen P. Peters, Joeri Rogelj, Chris Smith, Abigail C. Snyder, Isla R. Simpson, Abigail L. S. Swann, Claudia Tebaldi, Tatiana Ilyina, Carl-Friedrich Schleussner, Roland Séférian, Bjørn H. Samset, Detlef van Vuuren, and Sönke Zaehle
Geosci. Model Dev., 17, 8141–8172, https://doi.org/10.5194/gmd-17-8141-2024, https://doi.org/10.5194/gmd-17-8141-2024, 2024
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We discuss how, in order to provide more relevant guidance for climate policy, coordinated climate experiments should adopt a greater focus on simulations where Earth system models are provided with carbon emissions from fossil fuels together with land use change instructions, rather than past approaches that have largely focused on experiments with prescribed atmospheric carbon dioxide concentrations. We discuss how these goals might be achieved in coordinated climate modeling experiments.
Colin G. Jones, Fanny Adloff, Ben B. B. Booth, Peter M. Cox, Veronika Eyring, Pierre Friedlingstein, Katja Frieler, Helene T. Hewitt, Hazel A. Jeffery, Sylvie Joussaume, Torben Koenigk, Bryan N. Lawrence, Eleanor O'Rourke, Malcolm J. Roberts, Benjamin M. Sanderson, Roland Séférian, Samuel Somot, Pier Luigi Vidale, Detlef van Vuuren, Mario Acosta, Mats Bentsen, Raffaele Bernardello, Richard Betts, Ed Blockley, Julien Boé, Tom Bracegirdle, Pascale Braconnot, Victor Brovkin, Carlo Buontempo, Francisco Doblas-Reyes, Markus Donat, Italo Epicoco, Pete Falloon, Sandro Fiore, Thomas Frölicher, Neven S. Fučkar, Matthew J. Gidden, Helge F. Goessling, Rune Grand Graversen, Silvio Gualdi, José M. Gutiérrez, Tatiana Ilyina, Daniela Jacob, Chris D. Jones, Martin Juckes, Elizabeth Kendon, Erik Kjellström, Reto Knutti, Jason Lowe, Matthew Mizielinski, Paola Nassisi, Michael Obersteiner, Pierre Regnier, Romain Roehrig, David Salas y Mélia, Carl-Friedrich Schleussner, Michael Schulz, Enrico Scoccimarro, Laurent Terray, Hannes Thiemann, Richard A. Wood, Shuting Yang, and Sönke Zaehle
Earth Syst. Dynam., 15, 1319–1351, https://doi.org/10.5194/esd-15-1319-2024, https://doi.org/10.5194/esd-15-1319-2024, 2024
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We propose a number of priority areas for the international climate research community to address over the coming decade. Advances in these areas will both increase our understanding of past and future Earth system change, including the societal and environmental impacts of this change, and deliver significantly improved scientific support to international climate policy, such as future IPCC assessments and the UNFCCC Global Stocktake.
John Hillier, Adrian Champion, Tom Perkins, Freya Garry, and Hannah Bloomfield
Geosci. Commun., 7, 195–200, https://doi.org/10.5194/gc-7-195-2024, https://doi.org/10.5194/gc-7-195-2024, 2024
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To allow for more effective use of climate science, this work proposes and evaluates an open-access R code that deploys a measure of how natural hazards (e.g. extreme wind and flooding) co-occur, is obtainable from scientific research and is usable in practice without restricted data (climate or risk) being exposed. The approach can be applied to hazards in various sectors (e.g. road, rail and telecommunications).
Georgia Lazoglou, Theo Economou, Christina Anagnostopoulou, George Zittis, Anna Tzyrkalli, Pantelis Georgiades, and Jos Lelieveld
Geosci. Model Dev., 17, 4689–4703, https://doi.org/10.5194/gmd-17-4689-2024, https://doi.org/10.5194/gmd-17-4689-2024, 2024
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This study focuses on the important issue of the drizzle bias effect in regional climate models, described by an over-prediction of the number of rainy days while underestimating associated precipitation amounts. For this purpose, two distinct methodologies are applied and rigorously evaluated. These results are encouraging for using the multivariate machine learning method random forest to increase the accuracy of climate models concerning the projection of the number of wet days.
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The Pensions Regulator: Review shows how pension trustees are addressing climate risks and opportunities, press Release PN24-18, https://www.thepensionsregulator.gov.uk/en/media-hub/press -releases/2024-press-releases/review-shows-how-pension-trustees-are-addressing-climate-risks-and-opportunities (last access: 9 February 2026), 2024a. a
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The Pensions Regulator: Small DC schemes should improve or consolidate if they cannot protect savers from climate risk, press release PN25-15, https://www.thepensionsregulator.gov.uk/en/media-hub/press -releases/2025-press-releases/small-dc-schemes-should-improve-or-consolidate-if-they-cannot-protect-savers-from-climate-risk (last access: 9 February 2026), 2025. a
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Short summary
Extreme rainfall already creates significant financial risks for property investments in Europe, and these risks are expected to increase due to climate change. Using open data, we show how flooding resulting from extreme rainfall can damage assets and raise annual losses. The mix and location of assets in a property portfolio matter greatly, and practical measures to reduce flood damage can pay off quickly. Investors need to consider these risks to protect long‑term financial stability.
Extreme rainfall already creates significant financial risks for property investments in Europe,...
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