Articles | Volume 7, issue 2
https://doi.org/10.5194/wcd-7-633-2026
© 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/wcd-7-633-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
22 Apr 2026
Research article |
| 22 Apr 2026
| 22 Apr 2026
Understanding biases and changes in European heavy precipitation using dynamical flow precursors
Joshua Oldham-Dorrington
CORRESPONDING AUTHOR
Geophysical Institute, University of Bergen, Bergen, Norway
Bjerknes Centre for Climate Research, Bergen, Norway
Camille Li
Geophysical Institute, University of Bergen, Bergen, Norway
Bjerknes Centre for Climate Research, Bergen, Norway
Stefan Sobolowski
Geophysical Institute, University of Bergen, Bergen, Norway
Bjerknes Centre for Climate Research, Bergen, Norway
Robin Guillaume-Castel
Geophysical Institute, University of Bergen, Bergen, Norway
Bjerknes Centre for Climate Research, Bergen, Norway
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Cited articles
Abdelmoaty, H. M., Papalexiou, S. M., Rajulapati, C. R., and AghaKouchak, A.: Biases Beyond the Mean in CMIP6 Extreme Precipitation: A Global Investigation, Earth's Future, 9, e2021EF002196, https://doi.org/10.1029/2021EF002196, 2021. a
Addor, N., Rohrer, M., Furrer, R., and Seibert, J.: Propagation of Biases in Climate Models from the Synoptic to the Regional Scale: Implications for Bias Adjustment, J. Geophys. Res.-Atmos., 121, 2075–2089, https://doi.org/10.1002/2015JD024040, 2016. a
Beck, H. E., Wood, E. F., Pan, M., Fisher, C. K., Miralles, D. G., van Dijk, A. I. J. M., McVicar, T. R., and Adler, R. F.: MSWEP V2 Global 3-Hourly 0.1° Precipitation: Methodology and Quantitative Assessment, B. Am. Meteorol. Soc., 100, 473–500, https://doi.org/10.1175/BAMS-D-17-0138.1, 2019. a, b
Brands, S.: Common Error Patterns in the Regional Atmospheric Circulation Simulated by the CMIP Multi-Model Ensemble, Geophys. Res. Lett., 49, e2022GL101 446, https://doi.org/10.1029/2022GL101446, 2022. a
Cassano, J. J., Uotila, P., Lynch, A. H., and Cassano, E. N.: Predicted Changes in Synoptic Forcing of Net Precipitation in Large Arctic River Basins during the 21st Century, J. Geophys. Res.-Biogeo., 112, G04S49, https://doi.org/10.1029/2006JG000332, 2007. a
Cattiaux, J., Douville, H., and Peings, Y.: European Temperatures in CMIP5: Origins of Present-Day Biases and Future Uncertainties, Clim. Dynam., 41, 2889–2907, https://doi.org/10.1007/s00382-013-1731-y, 2013. a
Chericoni, M., Fosser, G., Flaounas, E., Gaetani, M., and Anav, A.: Unravelling Drivers of the Future Mediterranean Precipitation Paradox during Cyclones, npj Climate and Atmospheric Science, 8, 260, https://doi.org/10.1038/s41612-025-01121-w, 2025. a
Copernicus Climate Change Service: ERA5 hourly data on single levels from 1940 to present, Copernicus Climate Change Service (C3S) Climate Data Store (CDS) [data set], https://doi.org/10.24381/cds.adbb2d47, 2023. a
Danabasoglu, G., Deser, C., Rodgers, K., and Timmermann, A.: CESM2 Large Ensemble, NSF National Center for Atmospheric Research [data set], https://doi.org/10.26024/kgmp-c556, 2020a. a
Danabasoglu, G., Lamarque, J.-F., Bacmeister, J., Bailey, D. A., DuVivier, A. K., Edwards, J., Emmons, L. K., Fasullo, J., Garcia, R., Gettelman, A., Hannay, C., Holland, M. M., Large, W. G., Lauritzen, P. H., Lawrence, D. M., Lenaerts, J. T. M., Lindsay, K., Lipscomb, W. H., Mills, M. J., Neale, R., Oleson, K. W., Otto-Bliesner, B., Phillips, A. S., Sacks, W., Tilmes, S., van Kampenhout, L., Vertenstein, M., Bertini, A., Dennis, J., Deser, C., Fischer, C., Fox-Kemper, B., Kay, J. E., Kinnison, D., Kushner, P. J., Larson, V. E., Long, M. C., Mickelson, S., Moore, J. K., Nienhouse, E., Polvani, L., Rasch, P. J., and Strand, W. G.: The Community Earth System Model Version 2 (CESM2), J. Adv. Model. Earth Sy., 12, e2019MS001916, https://doi.org/10.1029/2019MS001916, 2020b. a, b
Deser, C., Terray, L., and Phillips, A. S.: Forced and Internal Components of Winter Air Temperature Trends over North America during the past 50 Years: Mechanisms and Implications, J. Climate, 29, 2237–2258, https://doi.org/10.1175/JCLI-D-15-0304.1, 2016. a, b, c
Doane-Solomon, R., Woollings, T., and Simpson, I. R.: Dynamic Contributions to Recent Observed Wintertime Precipitation Trends in Mediterranean-Type Climate Regions, Geophys. Res. Lett., 52, e2024GL114258, https://doi.org/10.1029/2024GL114258, 2025. a, b
Donat, M. G., Delgado-Torres, C., De Luca, P., Mahmood, R., Ortega, P., and Doblas-Reyes, F. J.: How Credibly Do CMIP6 Simulations Capture Historical Mean and Extreme Precipitation Changes?, Geophys. Res. Lett., 50, e2022GL102466, https://doi.org/10.1029/2022GL102466, 2023. a, b, c
Dorrington, J., Strommen, K., Fabiano, F., and Molteni, F.: CMIP6 Models Trend Toward Less Persistent European Blocking Regimes in a Warming Climate, Geophys. Res. Lett., 49, e2022GL100811, https://doi.org/10.1029/2022GL100811, 2022. a
Dorrington, J., Grams, C., Grazzini, F., Magnusson, L., and Vitart, F.: Domino: A New Framework for the Automated Identification of Weather Event Precursors, Demonstrated for European Extreme Rainfall, Q. J. Roy. Meteor. Soc., 150, 776–795, https://doi.org/10.1002/qj.4622, 2024a (code available at: https://github.com/joshdorrington/domino, last access: 16 April 2026). a, b, c, d, e, f, g
Dorrington, J., Wenta, M., Grazzini, F., Magnusson, L., Vitart, F., and Grams, C. M.: Precursors and pathways: dynamically informed extreme event forecasting demonstrated on the historic Emilia-Romagna 2023 flood, Nat. Hazards Earth Syst. Sci., 24, 2995–3012, https://doi.org/10.5194/nhess-24-2995-2024, 2024b. a, b, c
Driouech, F., Déqué, M., and Sánchez-Gómez, E.: Weather Regimes – Moroccan Precipitation Link in a Regional Climate Change Simulation, Global Planet. Change, 72, 1–10, https://doi.org/10.1016/j.gloplacha.2010.03.004, 2010. a
Du, Y., Wang, D., Zhu, J., Wang, D., Qi, X., and Cai, J.: Comprehensive Assessment of CMIP5 and CMIP6 Models in Simulating and Projecting Precipitation over the Global Land, Int. J. Climatol., 42, 6859–6875, https://doi.org/10.1002/joc.7616, 2022. a
EEA: Economic Losses and Fatalities from Weather- and Climate-Related Events in Europe – European Environment Agency, https://www.eea.europa.eu/publications/economic-losses-and-fatalities-from (last access: 17 April 2026), 2022. a
Fischer, L. J., Bresch, D. N., Büeler, D., Grams, C. M., Noyelle, R., Röthlisberger, M., and Wernli, H.: How relevant are frequency changes of weather regimes for understanding climate change signals in surface precipitation in the North Atlantic–European sector? A conceptual analysis with CESM1 large ensemble simulations, Weather Clim. Dynam., 6, 1027–1043, https://doi.org/10.5194/wcd-6-1027-2025, 2025. a, b
Gerighausen, J., Oldham-Dorrington, J., Mockert, F., Osman, M., and Grams, C. M.: Understanding and Anticipating Anomalous Surface Impacts During Large-Scale Regimes, Meteorol. Appl., 32, e70099, https://doi.org/10.1002/met.70099, 2025. a
Grazzini, F., Fragkoulidis, G., Teubler, F., Wirth, V., and Craig, G. C.: Extreme Precipitation Events over Northern Italy. Part II: Dynamical Precursors, Q. J. Roy. Meteor. Soc., 147, 1237–1257, https://doi.org/10.1002/qj.3969, 2021. a
Guo, R., Deser, C., Terray, L., and Lehner, F.: Human Influence on Winter Precipitation Trends (1921–2015) over North America and Eurasia Revealed by Dynamical Adjustment, Geophys. Res. Lett., 46, 3426–3434, https://doi.org/10.1029/2018GL081316, 2019. a
Held, I. M. and Soden, B. J.: Robust Responses of the Hydrological Cycle to Global Warming, J. Climate, 19, 5686–5699, https://doi.org/10.1175/JCLI3990.1, 2006. a, b
Hersbach, H., Bell, B., Berrisford, P., Hirahara, S., Horányi, A., Muñoz-Sabater, J., Nicolas, J., Peubey, C., Radu, R., Schepers, D., Simmons, A., Soci, C., Abdalla, S., Abellan, X., Balsamo, G., Bechtold, P., Biavati, G., Bidlot, J., Bonavita, M., De Chiara, G., Dahlgren, P., Dee, D., Diamantakis, M., Dragani, R., Flemming, J., Forbes, R., Fuentes, M., Geer, A., Haimberger, L., Healy, S., Hogan, R. J., Hólm, E., Janisková, M., Keeley, S., Laloyaux, P., Lopez, P., Lupu, C., Radnoti, G., de Rosnay, P., Rozum, I., Vamborg, F., Villaume, S., and Thépaut, J.-N.: The ERA5 Global Reanalysis, Q. J. Roy. Meteor. Soc., 146, 1999–2049, https://doi.org/10.1002/qj.3803, 2020. a, b
Hewson, T. D. and Pillosu, F. M.: A Low-Cost Post-Processing Technique Improves Weather Forecasts around the World, Communications Earth & Environment, 2, 132, https://doi.org/10.1038/s43247-021-00185-9, 2021. a
Iversen, E. C., Hodnebrog, Ø., Seland Graff, L., Nygaard, B. E., and Iversen, T.: Future Winter Precipitation Decreases Associated With the North Atlantic Warming Hole and Reduced Convection, J. Geophys. Res.-Atmos., 128, e2022JD038374, https://doi.org/10.1029/2022JD038374, 2023. a, b
Konstali, K., Spengler, T., Spensberger, C., and Sorteberg, A.: Linking Future Precipitation Changes to Weather Features in CESM2-LE, J. Geophys. Res.-Atmos., 129, e2024JD041190, https://doi.org/10.1029/2024JD041190, 2024. a
Kuma, P., Bender, F. A.-M., and Jönsson, A. R.: Climate Model Code Genealogy and Its Relation to Climate Feedbacks and Sensitivity, J. Adv. Model. Earth Sy., 15, e2022MS003588, https://doi.org/10.1029/2022MS003588, 2023. a
Mann, M. E., Steinman, B. A., and Miller, S. K.: Absence of Internal Multidecadal and Interdecadal Oscillations in Climate Model Simulations, Nat. Commun., 11, 49, https://doi.org/10.1038/s41467-019-13823-w, 2020. a
Maraun, D.: Nonstationarities of Regional Climate Model Biases in European Seasonal Mean Temperature and Precipitation Sums, Geophys. Res. Lett., 39, https://doi.org/10.1029/2012GL051210, 2012. a, b
Maraun, D., Shepherd, T. G., Widmann, M., Zappa, G., Walton, D., Gutiérrez, J. M., Hagemann, S., Richter, I., Soares, P. M. M., Hall, A., and Mearns, L. O.: Towards Process-Informed Bias Correction of Climate Change Simulations, Nat. Clim. Change, 7, 764–773, https://doi.org/10.1038/nclimate3418, 2017. a
Meehl, G. A., Arblaster, J. M., Bates, S., Richter, J. H., Tebaldi, C., Gettelman, A., Medeiros, B., Bacmeister, J., DeRepentigny, P., Rosenbloom, N., Shields, C., Hu, A., Teng, H., Mills, M. J., and Strand, G.: Characteristics of Future Warmer Base States in CESM2, Earth and Space Science, 7, e2020EA001296, https://doi.org/10.1029/2020EA001296, 2020. a, b
Muñoz-Sabater, J., Dutra, E., Agustí-Panareda, A., Albergel, C., Arduini, G., Balsamo, G., Boussetta, S., Choulga, M., Harrigan, S., Hersbach, H., Martens, B., Miralles, D. G., Piles, M., Rodríguez-Fernández, N. J., Zsoter, E., Buontempo, C., and Thépaut, J.-N.: ERA5-Land: a state-of-the-art global reanalysis dataset for land applications, Earth Syst. Sci. Data, 13, 4349–4383, https://doi.org/10.5194/essd-13-4349-2021, 2021. a
O'Gorman, P. A. and Schneider, T.: The Physical Basis for Increases in Precipitation Extremes in Simulations of 21st-Century Climate Change, P. Natl. Acad. Sci. USA, 106, 14773–14777, https://doi.org/10.1073/pnas.0907610106, 2009. a
Olonscheck, D., Suarez-Gutierrez, L., Milinski, S., Beobide-Arsuaga, G., Baehr, J., Fröb, F., Ilyina, T., Kadow, C., Krieger, D., Li, H., Marotzke, J., Plésiat, É., Schupfner, M., Wachsmann, F., Wallberg, L., Wieners, K.-H., and Brune, S.: The New Max Planck Institute Grand Ensemble With CMIP6 Forcing and High-Frequency Model Output, J. Adv. Model. Earth Sy., 15, e2023MS003790, https://doi.org/10.1029/2023MS003790, 2023. a, b
O'Neill, B. C., Tebaldi, C., van Vuuren, D. P., Eyring, V., Friedlingstein, P., Hurtt, G., Knutti, R., Kriegler, E., Lamarque, J.-F., Lowe, J., Meehl, G. A., Moss, R., Riahi, K., and Sanderson, B. M.: The Scenario Model Intercomparison Project (ScenarioMIP) for CMIP6, Geosci. Model Dev., 9, 3461–3482, https://doi.org/10.5194/gmd-9-3461-2016, 2016. a
O'Neill, B. C., Kriegler, E., Ebi, K. L., Kemp-Benedict, E., Riahi, K., Rothman, D. S., van Ruijven, B. J., van Vuuren, D. P., Birkmann, J., Kok, K., Levy, M., and Solecki, W.: The Roads Ahead: Narratives for Shared Socioeconomic Pathways Describing World Futures in the 21st Century, Global Environmental Change, 42, 169–180, https://doi.org/10.1016/j.gloenvcha.2015.01.004, 2017. a
Pfahl, S., O'Gorman, P. A., and Fischer, E. M.: Understanding the Regional Pattern of Projected Future Changes in Extreme Precipitation, Nat. Clim. Change, 7, 423–427, https://doi.org/10.1038/nclimate3287, 2017. a, b
Respati, M. R., Dommenget, D., Segura, H., and Stassen, C.: Diagnosing Drivers of Tropical Precipitation Biases in Coupled Climate Model Simulations, Clim. Dynam., 62, 8691–8709, https://doi.org/10.1007/s00382-024-07355-3, 2024. a
Scafetta, N.: CMIP6 GCM Validation Based on ECS and TCR Ranking for 21st Century Temperature Projections and Risk Assessment, Atmosphere, 14, 345, https://doi.org/10.3390/atmos14020345, 2023. a
Scaife, A. A. and Smith, D.: A Signal-to-Noise Paradox in Climate Science, npj Climate and Atmospheric Science, 1, 28, https://doi.org/10.1038/s41612-018-0038-4, 2018. a
Shepherd, T. G.: Atmospheric Circulation as a Source of Uncertainty in Climate Change Projections, Nat. Geosci., 7, 703–708, https://doi.org/10.1038/ngeo2253, 2014. a
Simpson, I. R., Bacmeister, J., Neale, R. B., Hannay, C., Gettelman, A., Garcia, R. R., Lauritzen, P. H., Marsh, D. R., Mills, M. J., Medeiros, B., and Richter, J. H.: An Evaluation of the Large-Scale Atmospheric Circulation and Its Variability in CESM2 and Other CMIP Models, J. Geophys. Res.-Atmos., 125, e2020JD032835, https://doi.org/10.1029/2020JD032835, 2020. a, b, c
Sippel, S., Meinshausen, N., Merrifield, A., Lehner, F., Pendergrass, A. G., Fischer, E., and Knutti, R.: Uncovering the Forced Climate Response from a Single Ensemble Member Using Statistical Learning, J. Climate, 32, 5677–5699, https://doi.org/10.1175/JCLI-D-18-0882.1, 2019. a
Sobolowski, S., Somot, S., Fernandez, J., Evin, G., Brands, S., Maraun, D., Kotlarski, S., Jury, M., Benestad, R. E., Teichmann, C., Christensen, O. B., Bülow, K., Buonomo, E., Katragkou, E., Steger, C., Sørland, S., Nikulin, G., McSweeney, C., Dobler, A., Palmer, T., Wilcke, R., Boé, J., Brunner, L., Ribes, A., Qasmi, S., Nabat, P., Sevault, F., and Oudar, T.: GCM Selection and Ensemble Design: Best Practices and Recommendations from the EURO-CORDEX Community, B. Am. Meteorol. Soc., 106, E1834–E1850, https://doi.org/10.1175/BAMS-D-23-0189.1, 2025. a
Stendel, M., Francis, J., White, R., Williams, P. D., and Woollings, T.: Chapter 15 – The Jet Stream and Climate Change, in: Climate Change (Third Edition), edited by: Letcher, T. M., Elsevier, 327–357, https://doi.org/10.1016/B978-0-12-821575-3.00015-3, ISBN 978-0-12-821575-3, 2021. a
Terray, L.: A dynamical adjustment perspective on extreme event attribution, Weather Clim. Dynam., 2, 971–989, https://doi.org/10.5194/wcd-2-971-2021, 2021. a
Thompson, V.: Circulation Analogues Cannot Identify Changes in Rainfall Extremes, Environ. Res. Lett., 20, 121003, https://doi.org/10.1088/1748-9326/ae20a6, 2025. a
Vautard, R., Cattiaux, J., Happé, T., Singh, J., Bonnet, R., Cassou, C., Coumou, D., D'Andrea, F., Faranda, D., Fischer, E., Ribes, A., Sippel, S., and Yiou, P.: Heat Extremes in Western Europe Increasing Faster than Simulated Due to Atmospheric Circulation Trends, Nat. Commun., 14, 6803, https://doi.org/10.1038/s41467-023-42143-3, 2023. a
Virtanen, P., Gommers, R., Oliphant, T. E., Haberland, M., Reddy, T., Cournapeau, D., Burovski, E., Peterson, P., Weckesser, W., Bright, J., van der Walt, S. J., Brett, M., Wilson, J., Millman, K. J., Mayorov, N., Nelson, A. R. J., Jones, E., Kern, R., Larson, E., Carey, C. J., Polat, İ., Feng, Y., Moore, E. W., VanderPlas, J., Laxalde, D., Perktold, J., Cimrman, R., Henriksen, I., Quintero, E. A., Harris, C. R., Archibald, A. M., Ribeiro, A. H., Pedregosa, F., and van Mulbregt, P.: SciPy 1.0: Fundamental Algorithms for Scientific Computing in Python, Nat. Methods, 17, 261–272, https://doi.org/10.1038/s41592-019-0686-2, 2020. a
Zhu, D., Pfahl, S., Knutti, R., and Fischer, E. M.: Future Extreme Precipitation May Shift to Colder Seasons in Northern Mid- and High Latitudes, Communications Earth & Environment, 6, 657, https://doi.org/10.1038/s43247-025-02651-0, 2025. a
Short summary
The future of heavy precipitation in Europe is uncertain, and precipitation can be poorly represented in climate models. To understand model heavy precipitation better we break it into two steps. Firstly, we assess how frequently rainfall-favouring weather patterns occur. Secondly, we assess how often heavy precipitation occurs during those patterns. By doing so, we better understand model bias and forced changes, making current climate models more usable now and easier to improve going forward.
The future of heavy precipitation in Europe is uncertain, and precipitation can be poorly...
