Articles | Volume 29, issue 1
https://doi.org/10.5194/hess-29-45-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/hess-29-45-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
06 Jan 2025
Research article |
| 06 Jan 2025
| 06 Jan 2025
Downscaling the probability of heavy rainfall over the Nordic countries
Rasmus E. Benestad
CORRESPONDING AUTHOR
Norwegian Meteorological Institute, Henrik Mohns plass 1, Oslo 0313, Norway
Kajsa M. Parding
Norwegian Meteorological Institute, Henrik Mohns plass 1, Oslo 0313, Norway
Andreas Dobler
Norwegian Meteorological Institute, Henrik Mohns plass 1, Oslo 0313, Norway
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A mathematical method known as common EOFs is not widely used within the climate research community, but it offers innovative ways of evaluating climate models. We show how common EOFs can be used to evaluate large ensembles of global climate model simulations and distill information about their ability to reproduce salient features of the regional climate. We can say that they represent a kind of machine learning (ML) for dealing with big data.
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We compared hourly and daily extreme precipitation across Norway from HARMONIE Climate models at convection-permitting 3 km (HCLIM3) and 12 km (HCLIM12) resolutions. HCLIM3 more accurately captures the extremes in most regions and seasons (except in summer). Its advantages are more pronounced for hourly extremes than for daily extremes. The results highlight the value of convection-permitting models in improving extreme-precipitation predictions and in helping the local society brace for extreme weather.
Rasmus E. Benestad, Abdelkader Mezghani, Julia Lutz, Andreas Dobler, Kajsa M. Parding, and Oskar A. Landgren
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We evaluate the skill of a regional climate model, HARMONIE-Climate, to capture the present-day characteristics of heavy precipitation in the Nordic region and investigate the added value provided by a convection-permitting model version. The higher model resolution improves the representation of hourly heavy- and extreme-precipitation events and their diurnal cycle. The results indicate the benefits of convection-permitting models for constructing climate change projections over the region.
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Revised manuscript not accepted
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Cited articles
Anonymous Referee: Referee Comment 1, Comment on egusphere-2024-1463, https://doi.org/10.5194/egusphere-2024-1463-RC1, 2024.
Barnett, T. P.: Comparison of Near-Surface Air Temperature Variability in 11 Coupled Global Climate Models, J. Climate, 12, 511–518, https://doi.org/10.1175/1520-0442(1999)012<0511:CONSAT>2.0.CO;2, 1999. a
Benestad, R.: Downscaling Climate Information, vol. 1, Oxford University Press, https://doi.org/10.1093/acrefore/9780190228620.013.27, 2016. a, b, c
Benestad, R.: Empirical-statistical downscaling of daily precipitation information in the Nordics, figshare [code and data set], https://doi.org/10.6084/m9.figshare.25809196.v2, 2024. a, b, c
Benestad, R., Sillmann, J., Thorarinsdottir, T. L., Guttorp, P., Mesquita, M. D. S., Tye, M. R., Uotila, P., Maule, C. F., Thejll, P., Drews, M., and Parding, K. M.: New vigour involving statisticians to overcome ensemble fatigue, Nat. Clim. Change, 7, 697–703, https://doi.org/10.1038/nclimate3393, 2017. a
Benestad, R. E.: A comparison between two empirical downscaling strategies, Int. J. Climatol., 21, 1645–1668, https://doi.org/10.1002/joc.703, 2001. a, b
Benestad, R. E.: A new global set of downscaled temperature scenarios, J. Climate, 24, 2080–2098, https://doi.org/10.1175/2010JCLI3687.1, 2011. a
Benestad, R. E., Hanssen-Bauer, I., and Chen, D.: Empirical-statistical downscaling, World Scientific, https://doi.org/10.1142/6908, 2008. a, b
Benestad, R. E., Chen, D., Mezghani, A., Fan, L., and Parding, K.: On using principal components to represent stations in empirical-statistical downscaling, Tellus A, 67, 28326, https://doi.org/10.3402/tellusa.v67.28326, 2015a. a, b
Benestad, R. E., Mezghani, A., and Parding, K. M.: esd V1.0, Zenodo, https://doi.org/10.5281/zenodo.29385, 2015b. a
Benestad, R. E., Senan, R., and Orsolini, Y.: The use of regression for assessing a seasonal forecast model experiment, Earth Syst. Dynam., 7, 851–861, https://doi.org/10.5194/esd-7-851-2016, 2016. a
Benestad, R. E., Oort, B. v., Justino, F., Stordal, F., Parding, K. M., Mezghani, A., Erlandsen, H. B., Sillmann, J., and Pereira-Flores, M. E.: Downscaling probability of long heatwaves based on seasonal mean daily maximum temperatures, Advances in Statistical Climatology, Meteorology and Oceanography, 4, 37–52, https://doi.org/10.5194/ascmo-4-37-2018, 2018. a
Benestad, R. E., Mezghani, A., Lutz, J., Dobler, A., Parding, K. M., and Landgren, O. A.: Various ways of using empirical orthogonal functions for climate model evaluation, Geosci. Model Dev., 16, 2899–2913, https://doi.org/10.5194/gmd-16-2899-2023, 2023. a, b, c
Benestad, R. E., Lussana, C., and Dobler, A.: A link between the global surface area receiving daily precipitation, wet-day frequency and probability of extreme rainfall, Discover Water, 4, 10, https://doi.org/10.1007/s43832-024-00063-3, 2024. a
Christensen, J. H. and Christensen, O. B.: A summary of the PRUDENCE model projections of changes in European climate by the end of this century, Climatic Change, 81, 7–30, https://doi.org/10.1007/s10584-006-9210-7, 2007. a
Christensen, J. H., Carter, T. R., Rummukainen, M., and Amanatidis, G.: Evaluating the performance and utility of regional climate models: the PRUDENCE project, Climatic Change, 81, 1–6, https://doi.org/10.1007/s10584-006-9211-6, 2007. a
Coles, S. G.: An Introduction to Statistical Modeling of Extreme Values, Springer, London, ISBN 978-1-84996-874-4, 2001. a
Deser, C., Knutti, R., Solomon, S., and Phillips, A. S.: Communication of the role of natural variability in future North American climate, Nat. Clim. Change, 2, 775–779, https://doi.org/10.1038/nclimate1562, 2012. a, b, c
Deser, C., Lehner, F., Rodgers, K. B., Ault, T., Delworth, T. L., DiNezio, P. N., Fiore, A., Frankignoul, C., Fyfe, J. C., Horton, D. E., Kay, J. E., Knutti, R., Lovenduski, N. S., Marotzke, J., McKinnon, K. A., Minobe, S., Randerson, J., Screen, J. A., Simpson, I. R., and Ting, M.: Insights from Earth system model initial-condition large ensembles and future prospects, Nat. Clim. Change, 10, 277–286, https://doi.org/10.1038/s41558-020-0731-2, 2020. a, b, c
Eyring, V., Bony, S., Meehl, G. A., Senior, C. A., Stevens, B., Stouffer, R. J., and Taylor, K. E.: Overview of the Coupled Model Intercomparison Project Phase 6 (CMIP6) experimental design and organization, Geosci. Model Dev., 9, 1937–1958, https://doi.org/10.5194/gmd-9-1937-2016, 2016. a
Goodess, C., Osborn, T., and Hulme, M.: The identification and evaluation of suitable scenario development methods for the estimation of future probabilities of extreme weather events, Technical Report 4, Tyndall Centre, School of Environemtal Sciences, Univ. East Anglia, Norwich, https://www.researchgate.net/publication/267829957_The_identification_and_evaluation_of_suitable_scenario_development_methods_for_the_estimation_of_future_probabilities_of_extreme_weather_events (last access: 23 December 2024), 2003. a, b
Gutiérrez, J. M., Maraun, D., Widmann, M., Huth, R., Hertig, E., Benestad, R., Roessler, O., Wibig, J., Wilcke, R., Kotlarski, S., Martín, D. S., Herrera, S., Bedia, J., Casanueva, A., Manzanas, R., Iturbide, M., Vrac, M., Dubrovsky, M., Ribalaygua, J., Pórtoles, J., Räty, O., Räisänen, J., Hingray, B., Raynaud, D., Casado, M. J., Ramos, P., Zerenner, T., Turco, M., Bosshard, T., Štěpánek, P., Bartholy, J., Pongracz, R., Keller, D. E., Fischer, A. M., Cardoso, R. M., Soares, P. M. M., Czernecki, B., and Pagé, C.: An intercomparison of a large ensemble of statistical downscaling methods over Europe: Results from the VALUE perfect predictor cross‐validation experiment, Int. J. Climatol., 39, 3750–3785, https://doi.org/10.1002/joc.5462, 2018. a
Gutowski Jr., W. J., Giorgi, F., Timbal, B., Frigon, A., Jacob, D., Kang, H.-S., Raghavan, K., Lee, B., Lennard, C., Nikulin, G., O'Rourke, E., Rixen, M., Solman, S., Stephenson, T., and Tangang, F.: WCRP COordinated Regional Downscaling EXperiment (CORDEX): a diagnostic MIP for CMIP6, Geosci. Model Dev., 9, 4087–4095, https://doi.org/10.5194/gmd-9-4087-2016, 2016. a
Hawkins, E. and Sutton, R.: The potential to narrow uncertainty in regional climate predictions, B. Am. Meteorol. Soc., 90, 1095, https://doi.org/10.1175/2009BAMS2607.1, 2009. a
Hersbach, H., Bell, B., Berrisford, P., Hirahara, S., Horanyi, A., Munoz‐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., 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., Holm, E., Janiskova, M., Keeley, S., Laloyaux, P., Lopez, P., Lupu, C., Radnoti, G., Rosnay, P., Rozum, I., Vamborg, F., Villaume, S., and Thepaut, J.: The ERA5 global reanalysis, Q. J. Roy. Meteor. Soc., 146, 1999–2049, https://doi.org/10.1002/qj.3803, 2020. a
IPCC: Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change, Tech. rep., Cambridge University Press, https://doi.org/10.1017/9781009157896, 2021. a, b, c, d
Jacob, D., Teichmann, C., Sobolowski, S., Katragkou, E., Anders, I., Belda, M., Benestad, R., Boberg, F., Buonomo, E., Cardoso, R. M., Casanueva, A., Christensen, O. B., Christensen, J. H., Coppola, E., De Cruz, L., Davin, E. L., Dobler, A., Domínguez, M., Fealy, R., Fernandez, J., Gaertner, M. A., García-Díez, M., Giorgi, F., Gobiet, A., Goergen, K., Gómez-Navarro, J. J., Alemán, J. J. G., Gutiérrez, C., Gutiérrez, J. M., Güttler, I., Haensler, A., Halenka, T., Jerez, S., Jiménez-Guerrero, P., Jones, R. G., Keuler, K., Kjellström, E., Knist, S., Kotlarski, S., Maraun, D., van Meijgaard, E., Mercogliano, P., Montávez, J. P., Navarra, A., Nikulin, G., de Noblet-Ducoudré, N., Panitz, H.-J., Pfeifer, S., Piazza, M., Pichelli, E., Pietikäinen, J.-P., Prein, A. F., Preuschmann, S., Rechid, D., Rockel, B., Romera, R., Sánchez, E., Sieck, K., Soares, P. M. M., Somot, S., Srnec, L., Sørland, S. L., Termonia, P., Truhetz, H., Vautard, R., Warrach-Sagi, K., and Wulfmeyer, V.: Regional climate downscaling over Europe: perspectives from the EURO-CORDEX community, Reg. Environ. Change, 20, 51, https://doi.org/10.1007/s10113-020-01606-9, 2020. a
Klein Tank, A. J. B. W., Konnen, G. P., Böhm, R., Demarée, G., Gocheva, A., Mileta, M., Pashiardis, S., Hejkrlik, L., Kern-Hansen, C., Heino, R., Bessemoulin, P., Müller-Westermeier, G., Tzanakou, M., Szalai, S., Pálsdóttir, T., Fitzgerald, D., Rubin, S., Capaldo, M., Maugeri, M., Leitass, A., Bukantis, A., Aberfeld, R., Engelen, A. F. V. v., Førland, E., Mietus, M., Coelho, F., Mares, C., Razuvaev, V., Nieplova, E., Cegnar, T., López, J. A., Dahlström, B., Moberg, A., Kirchhofer, W., Ceylan, A., Pachaliuk, O., Alexander, L. V., and Petrovic, P.: Daily dataset of 20th-century surface air temperature and precipitation series for the European Climate Assessment, Int. J. Climatol., 22, 1441–1453, 2002. a, b
Lussana, C., Benestad, R., and Dobler, A.: Changes in regional daily precipitation intensity and spatial structure from global reanalyses, Int. J. Climatol., 44, 1135–1153, https://doi.org/10.1002/joc.8375, 2024. a
Maraun, D. and Widmann, M.: Statistical downscaling and bias correction for climate research, Cambridge University Press, https://doi.org/10.1017/9781107588783, 2018. a
Maraun, D., Widmann, M., Gutiérrez, J. M., Kotlarski, S., Chandler, R. E., Hertig, E., Wibig, J., Huth, R., and Wilcke, R. A.: VALUE: A framework to validate downscaling approaches for climate change studies, Earth's Future, 3, 2014EF000259, https://doi.org/10.1002/2014EF000259, 2015. a, b, c
Mezghani, A., Dobler, A., Haugen, J. E., Benestad, R. E., Parding, K. M., Piniewski, M., Kardel, I., and Kundzewicz, Z. W.: CHASE-PL Climate Projection dataset over Poland – bias adjustment of EURO-CORDEX simulations, Earth Syst. Sci. Data, 9, 905–925, https://doi.org/10.5194/essd-9-905-2017, 2017. a
Mezghani, A., Dobler, A., Benestad, R., Haugen, J. E., Parding, K. M., Piniewski, M., and Kundzewicz, Z. W.: Sub-sampling impact on the climate change signal over Poland based on simulations from statistical and dynamical downscaling, J. Appl. Meteorol. Clim., 58, 1061–1078, https://doi.org/10.1175/JAMC-D-18-0179.1, 2019. a
Nychka, D., Hammerling, D., Sain, S., and Lenssen, N.: LatticeKrig: Multiresolution Kriging Based on Markov Random Fields, Boulder, CO, USA, https://doi.org/10.5065/D6HD7T1R, 2016. a, b
Oguz, E. A., Benestad, R. E., Parding, K. M., Depina, I., and Thakur, V.: Quantification of climate change impact on rainfall-induced shallow landslide susceptibility: a case study in central Norway, Georisk: Assessment and Management of Risk for Engineered Systems and Geohazards, 24 pp., https://doi.org/10.1080/17499518.2023.2283848, 2024. a
Papalexiou, S. M. and Koutsoyiannis, D.: Battle of extreme value distributions: A global survey on extreme daily rainfall: Survey on extreme daily rainfall, Water Resour. Res., 49, 187–201, https://doi.org/10.1029/2012WR012557, 2013. a
Parding, K. M., Benestad, R., Mezghani, A., and Erlandsen, H. B.: Statistical Projection of the North Atlantic Storm Tracks, J. Appl. Meteorol. Clim., 58, 1509–1522, https://doi.org/10.1175/JAMC-D-17-0348.1, 2019. a, b
Parding, K. M., Benestad, R. E., Dyrrdal, A. V., and Lutz, J.: A principal-component-based strategy for regionalisation of precipitation intensity–duration–frequency (IDF) statistics, Hydrol. Earth Syst. Sci., 27, 3719–3732, https://doi.org/10.5194/hess-27-3719-2023, 2023. a
Pryor, S., School, J. T., and Barthelmie, R. J.: Empirical downscaling of wind speed probability distributions, J. Geophys. Res., 110, D19109, https://doi.org/10.1029/2005JD005899, 2005. a
Pryor, S., School, J. T., and Barthelmie, R. J.: Winds of change? Projections of near-surface winds under climate change scenarios, Geophys. Res. Lett., 33, L11702, https://doi.org/10.1029/2006GL026000, 2006. a
Rampal, N., Hobeichi, S., Gibson, P. B., Baño-Medina, J., Abramowitz, G., Beucler, T., González-Abad, J., Chapman, W., Harder, P., and Gutiérrez, J. M.: Enhancing Regional Climate Downscaling through Advances in Machine Learning, Artificial Intelligence for the Earth Systems, 3, 230066, https://doi.org/10.1175/AIES-D-23-0066.1, 2024. a, b, c, d, e
Schulzweida, U.: CDO User Guide: Climate Data Operator, Version 2.0.0, October 2021, Tech. rep., MPI for Meteorology, https://code.mpimet.mpg.de/projects/cdo/embedded/cdo.pdf (last access: 24 December 2024), 2021. a
Takayabu, I., Kanamaru, H., Dairaku, K., Benestad, R., Storch, H. V., and Christensen, J. H.: Reconsidering the quality and utility of downscaling, J. Meteorol. Soc. Jpn., 94A, 31–45, https://doi.org/10.2151/jmsj.2015-042, 2015. a, b, c
Trenberth, K. E., Dai, A., Rasmussen, R. M., and Parsons, D. B.: The Changing Character of Precipitation, B. Am. Meteorol. Soc., 84, 1205–1218, https://doi.org/10.1175/BAMS-84-9-1205, 2003. a
Von Storch, H., Zorita, E., and Cubasch, U.: Downscaling of Global Climate Change Estimates to Regional Scales: An Application to Iberian Rainfall in Wintertime, J. Climate, 6, 1161–1171, https://doi.org/10.1175/1520-0442(1993)006<1161:DOGCCE>2.0.CO;2, 1993. a
Wallace, J. and Dickinson, R. E.: Empirical orthogonal representation of time series in the frequency domain: I. Theoretical considerations, J. Appl. Meteorol., 11, 887–892, https://doi.org/10.1175/1520-0450(1972)011<0887:EOROTS>2.0.CO;2, 1972. a, b
Wilby, R., Dawson, C., and Barrow, E.: sdsm – a decision support tool for the assessment of regional climate change impacts, Environ. Modell. Softw., 17, 145–157, https://doi.org/10.1016/S1364-8152(01)00060-3, 2002. a
Wilks, D. S.: Statistical methods in the atmospheric sciences, vol. 91, International Geophysics Series, Academic Press, Amsterdam, Boston, 2nd edn., ISBN 978-0-12-751966-1, 2006. a
Ye, L., Hanson, L. S., Ding, P., Wang, D., and Vogel, R. M.: The probability distribution of daily precipitation at the point and catchment scales in the United States, Hydrol. Earth Syst. Sci., 22, 6519–6531, https://doi.org/10.5194/hess-22-6519-2018, 2018. a
Short summary
We present a new method to calculate the chance of heavy downpour and the maximum rainfall expected over a 25-year period. It is designed to analyse global climate models' reproduction of past and future climates. For the Nordic countries, it projects a wetter climate in the future with increased intensity but not necessarily more wet days. The analysis also shows that rainfall intensity is sensitive to future greenhouse gas emissions, while the number of wet days appears to be less affected.
We present a new method to calculate the chance of heavy downpour and the maximum rainfall...
