Articles | Volume 29, issue 20
https://doi.org/10.5194/hess-29-5695-2025
© 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-29-5695-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Impact of bias adjustment strategy on ensemble projections of hydrological extremes
WSL Institute for Snow and Avalanche Research SLF, Davos Dorf, Switzerland
Institute for Atmospheric and Climate Science, ETH Zurich, Zurich, Switzerland
Climate Change, Extremes and Natural Hazards in Alpine Regions Research Center CERC, Davos Dorf, Switzerland
Raul R. Wood
WSL Institute for Snow and Avalanche Research SLF, Davos Dorf, Switzerland
Institute for Atmospheric and Climate Science, ETH Zurich, Zurich, Switzerland
Climate Change, Extremes and Natural Hazards in Alpine Regions Research Center CERC, Davos Dorf, Switzerland
Mathieu Vrac
Laboratoire des Sciences du Climat et de l'Environnement (LSCE-IPSL), CEA/CNRS/UVSQ, Université Paris-Saclay, Centre d'Etudes de Saclay, Orme des Merisiers, 91191 Gif-sur-Yvette, France
Sven Kotlarski
Federal Office of Meteorology and Climatology MeteoSwiss, Zurich-Airport, Switzerland
Pradeebane Vaittinada Ayar
Laboratoire des Sciences du Climat et de l'Environnement (LSCE-IPSL), CEA/CNRS/UVSQ, Université Paris-Saclay, Centre d'Etudes de Saclay, Orme des Merisiers, 91191 Gif-sur-Yvette, France
Bastien François
Royal Netherlands Meteorological Institute (KNMI), Research and Development Weather and Climate (RDWK), De Bilt, the Netherlands
Manuela I. Brunner
WSL Institute for Snow and Avalanche Research SLF, Davos Dorf, Switzerland
Institute for Atmospheric and Climate Science, ETH Zurich, Zurich, Switzerland
Climate Change, Extremes and Natural Hazards in Alpine Regions Research Center CERC, Davos Dorf, Switzerland
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Hydrol. Earth Syst. Sci., 27, 2479–2497, https://doi.org/10.5194/hess-27-2479-2023, https://doi.org/10.5194/hess-27-2479-2023, 2023
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Lina Teckentrup, Martin G. De Kauwe, Gab Abramowitz, Andrew J. Pitman, Anna M. Ukkola, Sanaa Hobeichi, Bastien François, and Benjamin Smith
Earth Syst. Dynam., 14, 549–576, https://doi.org/10.5194/esd-14-549-2023, https://doi.org/10.5194/esd-14-549-2023, 2023
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Nat. Hazards Earth Syst. Sci., 23, 1313–1333, https://doi.org/10.5194/nhess-23-1313-2023, https://doi.org/10.5194/nhess-23-1313-2023, 2023
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Manuela Irene Brunner and Philippe Naveau
Hydrol. Earth Syst. Sci., 27, 673–687, https://doi.org/10.5194/hess-27-673-2023, https://doi.org/10.5194/hess-27-673-2023, 2023
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Bastien François and Mathieu Vrac
Nat. Hazards Earth Syst. Sci., 23, 21–44, https://doi.org/10.5194/nhess-23-21-2023, https://doi.org/10.5194/nhess-23-21-2023, 2023
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Compound events (CEs) result from a combination of several climate phenomena. In this study, we propose a new methodology to assess the time of emergence of CE probabilities and to quantify the contribution of marginal and dependence properties of climate phenomena to the overall CE probability changes. By applying our methodology to two case studies, we show the importance of considering changes in both marginal and dependence properties for future risk assessments related to CEs.
Antoine Grisart, Mathieu Casado, Vasileios Gkinis, Bo Vinther, Philippe Naveau, Mathieu Vrac, Thomas Laepple, Bénédicte Minster, Frederic Prié, Barbara Stenni, Elise Fourré, Hans Christian Steen-Larsen, Jean Jouzel, Martin Werner, Katy Pol, Valérie Masson-Delmotte, Maria Hoerhold, Trevor Popp, and Amaelle Landais
Clim. Past, 18, 2289–2301, https://doi.org/10.5194/cp-18-2289-2022, https://doi.org/10.5194/cp-18-2289-2022, 2022
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This paper presents a compilation of high-resolution (11 cm) water isotopic records, including published and new measurements, for the last 800 000 years from the EPICA Dome C ice core, Antarctica. Using this new combined water isotopes (δ18O and δD) dataset, we study the variability and possible influence of diffusion at the multi-decadal to multi-centennial scale. We observe a stronger variability at the onset of the interglacial interval corresponding to a warm period.
Pradeebane Vaittinada Ayar, Laurent Bopp, Jim R. Christian, Tatiana Ilyina, John P. Krasting, Roland Séférian, Hiroyuki Tsujino, Michio Watanabe, Andrew Yool, and Jerry Tjiputra
Earth Syst. Dynam., 13, 1097–1118, https://doi.org/10.5194/esd-13-1097-2022, https://doi.org/10.5194/esd-13-1097-2022, 2022
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The El Niño–Southern Oscillation is the main driver for the natural variability of global atmospheric CO2. It modulates the CO2 fluxes in the tropical Pacific with anomalous CO2 influx during El Niño and outflux during La Niña. This relationship is projected to reverse by half of Earth system models studied here under the business-as-usual scenario. This study shows models that simulate a positive bias in surface carbonate concentrations simulate a shift in the ENSO–CO2 flux relationship.
Veit Blauhut, Michael Stoelzle, Lauri Ahopelto, Manuela I. Brunner, Claudia Teutschbein, Doris E. Wendt, Vytautas Akstinas, Sigrid J. Bakke, Lucy J. Barker, Lenka Bartošová, Agrita Briede, Carmelo Cammalleri, Ksenija Cindrić Kalin, Lucia De Stefano, Miriam Fendeková, David C. Finger, Marijke Huysmans, Mirjana Ivanov, Jaak Jaagus, Jiří Jakubínský, Svitlana Krakovska, Gregor Laaha, Monika Lakatos, Kiril Manevski, Mathias Neumann Andersen, Nina Nikolova, Marzena Osuch, Pieter van Oel, Kalina Radeva, Renata J. Romanowicz, Elena Toth, Mirek Trnka, Marko Urošev, Julia Urquijo Reguera, Eric Sauquet, Aleksandra Stevkov, Lena M. Tallaksen, Iryna Trofimova, Anne F. Van Loon, Michelle T. H. van Vliet, Jean-Philippe Vidal, Niko Wanders, Micha Werner, Patrick Willems, and Nenad Živković
Nat. Hazards Earth Syst. Sci., 22, 2201–2217, https://doi.org/10.5194/nhess-22-2201-2022, https://doi.org/10.5194/nhess-22-2201-2022, 2022
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Recent drought events caused enormous damage in Europe. We therefore questioned the existence and effect of current drought management strategies on the actual impacts and how drought is perceived by relevant stakeholders. Over 700 participants from 28 European countries provided insights into drought hazard and impact perception and current management strategies. The study concludes with an urgent need to collectively combat drought risk via a European macro-level drought governance approach.
Moctar Dembélé, Mathieu Vrac, Natalie Ceperley, Sander J. Zwart, Josh Larsen, Simon J. Dadson, Grégoire Mariéthoz, and Bettina Schaefli
Hydrol. Earth Syst. Sci., 26, 1481–1506, https://doi.org/10.5194/hess-26-1481-2022, https://doi.org/10.5194/hess-26-1481-2022, 2022
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Climate change impacts on water resources in the Volta River basin are investigated under various global warming scenarios. Results reveal contrasting changes in future hydrological processes and water availability, depending on greenhouse gas emission scenarios, with implications for floods and drought occurrence over the 21st century. These findings provide insights for the elaboration of regional adaptation and mitigation strategies for climate change.
Manuela I. Brunner and Louise J. Slater
Hydrol. Earth Syst. Sci., 26, 469–482, https://doi.org/10.5194/hess-26-469-2022, https://doi.org/10.5194/hess-26-469-2022, 2022
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Assessing the rarity and magnitude of very extreme flood events occurring less than twice a century is challenging due to the lack of observations of such rare events. Here we develop a new approach, pooling reforecast ensemble members from the European Flood Awareness System to increase the sample size available to estimate the frequency of extreme flood events. We demonstrate that such ensemble pooling produces more robust estimates than observation-based estimates.
Álvaro Ossandón, Manuela I. Brunner, Balaji Rajagopalan, and William Kleiber
Hydrol. Earth Syst. Sci., 26, 149–166, https://doi.org/10.5194/hess-26-149-2022, https://doi.org/10.5194/hess-26-149-2022, 2022
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Timely projections of seasonal streamflow extremes on a river network can be useful for flood risk mitigation, but this is challenging, particularly under space–time nonstationarity. We develop a space–time Bayesian hierarchical model (BHM) using temporal climate covariates and copulas to project seasonal streamflow extremes and the attendant uncertainties. We demonstrate this on the Upper Colorado River basin to project spring flow extremes using the preceding winter’s climate teleconnections.
Yoann Robin and Mathieu Vrac
Earth Syst. Dynam., 12, 1253–1273, https://doi.org/10.5194/esd-12-1253-2021, https://doi.org/10.5194/esd-12-1253-2021, 2021
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We propose a new multivariate downscaling and bias correction approach called
time-shifted multivariate bias correction, which aims to correct temporal dependencies in addition to inter-variable and spatial ones. Our method is evaluated in a
perfect model experimentcontext where simulations are used as pseudo-observations. The results show a large reduction of the biases in the temporal properties, while inter-variable and spatial dependence structures are still correctly adjusted.
Cedric G. Ngoungue Langue, Christophe Lavaysse, Mathieu Vrac, Philippe Peyrillé, and Cyrille Flamant
Weather Clim. Dynam., 2, 893–912, https://doi.org/10.5194/wcd-2-893-2021, https://doi.org/10.5194/wcd-2-893-2021, 2021
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This work assesses the forecast of the temperature over the Sahara, a key driver of the West African Monsoon, at a seasonal timescale. The seasonal models are able to reproduce the climatological state and some characteristics of the temperature during the rainy season in the Sahel. But, because of errors in the timing, the forecast skill scores are significant only for the first 4 weeks.
Anna Denvil-Sommer, Marion Gehlen, and Mathieu Vrac
Ocean Sci., 17, 1011–1030, https://doi.org/10.5194/os-17-1011-2021, https://doi.org/10.5194/os-17-1011-2021, 2021
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In this work we explored design options for a future Atlantic-scale observational network enabling the release of carbon system estimates by combining data streams from various platforms. We used outputs of a physical–biogeochemical global ocean model at sites of real-world observations to reconstruct surface ocean pCO2 by applying a non-linear feed-forward neural network. The results provide important information for future BGC-Argo deployment, i.e. important regions and the number of floats.
Paul C. Astagneau, Guillaume Thirel, Olivier Delaigue, Joseph H. A. Guillaume, Juraj Parajka, Claudia C. Brauer, Alberto Viglione, Wouter Buytaert, and Keith J. Beven
Hydrol. Earth Syst. Sci., 25, 3937–3973, https://doi.org/10.5194/hess-25-3937-2021, https://doi.org/10.5194/hess-25-3937-2021, 2021
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The R programming language has become an important tool for many applications in hydrology. In this study, we provide an analysis of some of the R tools providing hydrological models. In total, two aspects are uniformly investigated, namely the conceptualisation of the models and the practicality of their implementation for end-users. These comparisons aim at easing the choice of R tools for users and at improving their usability for hydrology modelling to support more transferable research.
Manuela I. Brunner, Eric Gilleland, and Andrew W. Wood
Earth Syst. Dynam., 12, 621–634, https://doi.org/10.5194/esd-12-621-2021, https://doi.org/10.5194/esd-12-621-2021, 2021
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Compound hot and dry events can lead to severe impacts whose severity may depend on their timescale and spatial extent. Here, we show that the spatial extent and timescale of compound hot–dry events are strongly related, spatial compound event extents are largest at
sub-seasonal timescales, and short events are driven more by high temperatures, while longer events are more driven by low precipitation. Future climate impact studies should therefore be performed at different timescales.
Michael Matiu, Alice Crespi, Giacomo Bertoldi, Carlo Maria Carmagnola, Christoph Marty, Samuel Morin, Wolfgang Schöner, Daniele Cat Berro, Gabriele Chiogna, Ludovica De Gregorio, Sven Kotlarski, Bruno Majone, Gernot Resch, Silvia Terzago, Mauro Valt, Walter Beozzo, Paola Cianfarra, Isabelle Gouttevin, Giorgia Marcolini, Claudia Notarnicola, Marcello Petitta, Simon C. Scherrer, Ulrich Strasser, Michael Winkler, Marc Zebisch, Andrea Cicogna, Roberto Cremonini, Andrea Debernardi, Mattia Faletto, Mauro Gaddo, Lorenzo Giovannini, Luca Mercalli, Jean-Michel Soubeyroux, Andrea Sušnik, Alberto Trenti, Stefano Urbani, and Viktor Weilguni
The Cryosphere, 15, 1343–1382, https://doi.org/10.5194/tc-15-1343-2021, https://doi.org/10.5194/tc-15-1343-2021, 2021
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The first Alpine-wide assessment of station snow depth has been enabled by a collaborative effort of the research community which involves more than 30 partners, 6 countries, and more than 2000 stations. It shows how snow in the European Alps matches the climatic zones and gives a robust estimate of observed changes: stronger decreases in the snow season at low elevations and in spring at all elevations, however, with considerable regional differences.
Manuela I. Brunner, Lieke A. Melsen, Andrew W. Wood, Oldrich Rakovec, Naoki Mizukami, Wouter J. M. Knoben, and Martyn P. Clark
Hydrol. Earth Syst. Sci., 25, 105–119, https://doi.org/10.5194/hess-25-105-2021, https://doi.org/10.5194/hess-25-105-2021, 2021
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Assessments of current, local, and regional flood hazards and their future changes often involve the use of hydrologic models. A reliable model ideally reproduces both local flood characteristics and regional aspects of flooding. In this paper we investigate how such characteristics are represented by hydrologic models. Our results show that both the modeling of local and regional flood characteristics are challenging, especially under changing climate conditions.
Mathieu Vrac and Soulivanh Thao
Geosci. Model Dev., 13, 5367–5387, https://doi.org/10.5194/gmd-13-5367-2020, https://doi.org/10.5194/gmd-13-5367-2020, 2020
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We propose a multivariate bias correction (MBC) method to adjust the spatial and/or inter-variable properties of climate simulations, while also accounting for their temporal dependences (e.g., autocorrelations).
It consists on a method reordering the ranks of the time series according to their multivariate distance to a reference time series.
Results show that temporal correlations are improved while spatial and inter-variable correlations are still satisfactorily corrected.
Cited articles
Aalbers, E. E., Lenderink, G., van Meijgaard, E., and van den Hurk, B. J. J. M.: Local-scale changes in mean and heavy precipitation in Western Europe, climate change or internal variability?, Climate Dynamics, 50, 4745–4766, https://doi.org/10.1007/s00382-017-3901-9, 2017. a
Allard, D., Vrac, M., François, B., and García de Cortázar-Atauri, I.: Assessing multivariate bias corrections of climate simulations on various impact models under climate change, Hydrol. Earth Syst. Sci., 29, 4711–4738, https://doi.org/10.5194/hess-29-4711-2025, 2025. a
Berghuijs, W. R., Allen, S. T., Harrigan, S., and Kirchner, J. W.: Growing Spatial Scales of Synchronous River Flooding in Europe, Geophysical Research Letters, 46, 1423–1428, https://doi.org/10.1029/2018gl081883, 2019. a
Bergström, S. and Forsman, A.: Development of a conceptual deterministic rainfall-runoff model, Hydrology Research, 4, 147–170, https://doi.org/10.2166/nh.1973.0012, 1973. a
Bertola, M., Viglione, A., Lun, D., Hall, J., and Blöschl, G.: Flood trends in Europe: are changes in small and big floods different?, Hydrol. Earth Syst. Sci., 24, 1805–1822, https://doi.org/10.5194/hess-24-1805-2020, 2020. a
Bevacqua, E., Suarez-Gutierrez, L., Jézéquel, A., Lehner, F., Vrac, M., Yiou, P., and Zscheischler, J.: Advancing research on compound weather and climate events via large ensemble model simulations, Nature Communications, 14, https://doi.org/10.1038/s41467-023-37847-5, 2023. a
Blöschl, G., Gaál, L., Hall, J., Kiss, A., Komma, J., Nester, T., Parajka, J., Perdigão, R. A. P., Plavcová, L., Rogger, M., Salinas, J. L., and Viglione, A.: Increasing river floods: fiction or reality?, WIREs Water, 2, 329–344, https://doi.org/10.1002/wat2.1079, 2015. a
Brunner, M. I., Gilleland, E., and Wood, A. W.: Space–time dependence of compound hot–dry events in the United States: assessment using a multi-site multi-variable weather generator, Earth Syst. Dynam., 12, 621–634, https://doi.org/10.5194/esd-12-621-2021, 2021a. a
Brunner, M. I., Swain, D. L., Wood, R. R., Willkofer, F., Done, J. M., Gilleland, E., and Ludwig, R.: An extremeness threshold determines the regional response of floods to changes in rainfall extremes, Communications Earth and Environment, 2, https://doi.org/10.1038/s43247-021-00248-x, 2021b. a
Bruno, G., Avanzi, F., Alfieri, L., Libertino, A., Gabellani, S., and Duethmann, D.: Hydrological model skills change with drought severity; insights from multi-variable evaluation, Journal of Hydrology, 634, 131023, https://doi.org/10.1016/j.jhydrol.2024.131023, 2024. a
Cannon, A. J.: Multivariate quantile mapping bias correction: an N-dimensional probability density function transform for climate model simulations of multiple variables, Climate Dynamics, 50, 31–49, https://doi.org/10.1007/s00382-017-3580-6, 2017. a, b
Cannon, A. J., Alford, H., Shrestha, R. R., Kirchmeier‐Young, M. C., and Najafi, M. R.: Canadian Large Ensembles Adjusted Dataset version 1 (CanLEADv1): Multivariate bias‐corrected climate model outputs for terrestrial modelling and attribution studies in North America, Geoscience Data Journal, 9, 288–303, https://doi.org/10.1002/gdj3.142, 2021. a, b
Chadwick, C., Gironás, J., González-Leiva, F., and Aedo, S.: Bias adjustment to preserve changes in variability: the unbiased mapping of GCM changes, Hydrological Sciences Journal, 68, 1184–1201, https://doi.org/10.1080/02626667.2023.2201450, 2023. a
Chen, J., Li, C., Brissette, F. P., Chen, H., Wang, M., and Essou, G. R.: Impacts of correcting the inter-variable correlation of climate model outputs on hydrological modeling, Journal of Hydrology, 560, 326–341, https://doi.org/10.1016/j.jhydrol.2018.03.040, 2018. a, b
Chen, J., Brissette, F. P., Zhang, X. J., Chen, H., Guo, S., and Zhao, Y.: Bias correcting climate model multi-member ensembles to assess climate change impacts on hydrology, Climatic Change, 153, 361–377, https://doi.org/10.1007/s10584-019-02393-x, 2019. a, b, c, d
Clark, M. P., Wilby, R. L., Gutmann, E. D., Vano, J. A., Gangopadhyay, S., Wood, A. W., Fowler, H. J., Prudhomme, C., Arnold, J. R., and Brekke, L. D.: Characterizing Uncertainty of the Hydrologic Impacts of Climate Change, Current Climate Change Reports, 2, 55–64, https://doi.org/10.1007/s40641-016-0034-x, 2016. a, b
Coron, L., Delaigue, O., Thirel, G., Dorchies, D., Perrin, C., and Michel, C.: airGR: Suite of GR Hydrological Models for Precipitation-Runoff Modelling. R package version 1.7.6., https://doi.org/10.15454/EX11NA, 2020. a
Dai, A.: Increasing drought under global warming in observations and models, Nature Climate Change, 3, 52–58, https://doi.org/10.1038/nclimate1633, 2012. a
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, Nature Climate Change, 10, 277–286, https://doi.org/10.1038/s41558-020-0731-2, 2020. a, b, c
Déqué, M.: Frequency of precipitation and temperature extremes over France in an anthropogenic scenario: Model results and statistical correction according to observed values, Global and Planetary Change, 57, 16–26, https://doi.org/10.1016/j.gloplacha.2006.11.030, 2007. a
Faghih, M. and Brissette, F.: The role of internal climate variability on future streamflow projections, Journal of Hydrology, 625, 130 101, https://doi.org/10.1016/j.jhydrol.2023.130101, 2023. a
Fang, B., Bevacqua, E., Rakovec, O., and Zscheischler, J.: An increase in the spatial extent of European floods over the last 70 years, Hydrology and Earth System Sciences, 28, 3755–3775, https://doi.org/10.5194/hess-28-3755-2024, 2024. a
Federal office for the Environment: Hydrological Data Service for Watercourses and Lakes, https://www.bafu.admin.ch/bafu/en/home/topics/water/state/data/obtaining-monitoring-data-on-the-topic-of-water/hydrological-data-service-for-watercourses-and-lakes.html (last access: 21 July 2024), 2024. a
Feigenwinter, I., Kotlarski, S., Casanueva, A., Schwierz, C., and Liniger, M.: Exploring quantile mapping as a tool to produce user-tailored climate scenarios for Switzerland, Tech. Rep. 270, MeteoSchweiz, https://www.meteosuisse.admin.ch/dam/jcr:1b810050-11a2-415d-b439-3b2eb75f9693/MeteoSchweiz_Fachbericht_270_final.pdf (last access: 21 July 2024), 2018. a
François, B., Vrac, M., Cannon, A. J., Robin, Y., and Allard, D.: Multivariate bias corrections of climate simulations: which benefits for which losses?, Earth Syst. Dynam., 11, 537–562, https://doi.org/10.5194/esd-11-537-2020, 2020. a, b, c, d
François, B., Thao, S., and Vrac, M.: Adjusting spatial dependence of climate model outputs with cycle-consistent adversarial networks, Climate Dynamics, 57, 3323–3353, https://doi.org/10.1007/s00382-021-05869-8, 2021. a
Frei, C.: Interpolation of temperature in a mountainous region using nonlinear profiles and non‐Euclidean distances, International Journal of Climatology, 34, 1585–1605, https://doi.org/10.1002/joc.3786, 2013. a
Frei, C. and Schär, C.: A precipitation climatology of the Alps from high-resolution rain-gauge observations, International Journal of Climatology, 18, 873–900, https://doi.org/10.1002/(sici)1097-0088(19980630)18:8<873::aid-joc255>3.0.co;2-9, 1998. a
Fyfe, J. C., Derksen, C., Mudryk, L., Flato, G. M., Santer, B. D., Swart, N. C., Molotch, N. P., Zhang, X., Wan, H., Arora, V. K., Scinocca, J., and Jiao, Y.: Large near-term projected snowpack loss over the western United States, Nature Communications, 8, https://doi.org/10.1038/ncomms14996, 2017. a
Gelfan, A., Semenov, V. A., Gusev, E., Motovilov, Y., Nasonova, O., Krylenko, I., and Kovalev, E.: Large-basin hydrological response to climate model outputs: uncertainty caused by internal atmospheric variability, Hydrol. Earth Syst. Sci., 19, 2737–2754, https://doi.org/10.5194/hess-19-2737-2015, 2015. a
Gudmundsson, L., Bremnes, J. B., Haugen, J. E., and Engen-Skaugen, T.: Technical Note: Downscaling RCM precipitation to the station scale using statistical transformations – a comparison of methods, Hydrol. Earth Syst. Sci., 16, 3383–3390, https://doi.org/10.5194/hess-16-3383-2012, 2012. a
Guo, Q., Chen, J., Zhang, X. J., Xu, C., and Chen, H.: Impacts of Using State‐of‐the‐Art Multivariate Bias Correction Methods on Hydrological Modeling Over North America, Water Resources Research, 56, https://doi.org/10.1029/2019wr026659, 2020. a
Gupta, H. V., Kling, H., Yilmaz, K. K., and Martinez, G. F.: Decomposition of the mean squared error and NSE performance criteria: Implications for improving hydrological modelling, Journal of Hydrology, 377, 80–91, https://doi.org/10.1016/j.jhydrol.2009.08.003, 2009. a
Hagemann, S., Chen, C., Haerter, J. O., Heinke, J., Gerten, D., and Piani, C.: Impact of a Statistical Bias Correction on the Projected Hydrological Changes Obtained from Three GCMs and Two Hydrology Models, Journal of Hydrometeorology, 12, 556–578, https://doi.org/10.1175/2011jhm1336.1, 2011. a, b
Hakala, K., Addor, N., and Seibert, J.: Hydrological Modeling to Evaluate Climate Model Simulations and Their Bias Correction, Journal of Hydrometeorology, 19, 1321–1337, https://doi.org/10.1175/jhm-d-17-0189.1, 2018. a
Hallegatte, S.: A Cost Effective Solution to Reduce Disaster Losses in Developing Countries: Hydro-Meteorological Services, Early Warning, and Evacuation, The World Bank, https://doi.org/10.1596/1813-9450-6058, 2012. a
Hempel, S., Frieler, K., Warszawski, L., Schewe, J., and Piontek, F.: A trend-preserving bias correction – the ISI-MIP approach, Earth Syst. Dynam., 4, 219–236, https://doi.org/10.5194/esd-4-219-2013, 2013. a, b
Höge, M., Kauzlaric, M., Siber, R., Schönenberger, U., Horton, P., Schwanbeck, J., Floriancic, M. G., Viviroli, D., Wilhelm, S., Sikorska-Senoner, A. E., Addor, N., Brunner, M., Pool, S., Zappa, M., and Fenicia, F.: CAMELS-CH: hydro-meteorological time series and landscape attributes for 331 catchments in hydrologic Switzerland, Earth Syst. Sci. Data, 15, 5755–5784, https://doi.org/10.5194/essd-15-5755-2023, 2023. a, b
Ivanov, M. A., Luterbacher, J., and Kotlarski, S.: Climate Model Biases and Modification of the Climate Change Signal by Intensity-Dependent Bias Correction, Journal of Climate, 31, 6591–6610, https://doi.org/10.1175/jcli-d-17-0765.1, 2018. a
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, Regional Environmental Change, 20, https://doi.org/10.1007/s10113-020-01606-9, 2020. a, b
Jakob Themeßl, M., Gobiet, A., and Leuprecht, A.: Empirical‐statistical downscaling and error correction of daily precipitation from regional climate models, International Journal of Climatology, 31, 1530–1544, https://doi.org/10.1002/joc.2168, 2011. a
Johnson, F. and Sharma, A.: What are the impacts of bias correction on future drought projections?, Journal of Hydrology, 525, 472–485, https://doi.org/10.1016/j.jhydrol.2015.04.002, 2015. a
Kemter, M., Merz, B., Marwan, N., Vorogushyn, S., and Blöschl, G.: Joint Trends in Flood Magnitudes and Spatial Extents Across Europe, Geophysical Research Letters, 47, https://doi.org/10.1029/2020gl087464, 2020. a
Kirchmeier-Young, M. C., Zwiers, F. W., Gillett, N. P., and Cannon, A. J.: Attributing extreme fire risk in Western Canada to human emissions, Climatic Change, 144, 365–379, https://doi.org/10.1007/s10584-017-2030-0, 2017. a, b, c, d
Kraft, B., Schirmer, M., Aeberhard, W. H., Zappa, M., Seneviratne, S. I., and Gudmundsson, L.: CH-RUN: a deep-learning-based spatially contiguous runoff reconstruction for Switzerland, Hydrol. Earth Syst. Sci., 29, 1061–1082, https://doi.org/10.5194/hess-29-1061-2025, 2025. a, b
Le Moine, N.: Le bassin versant de surface vu par le souterrain: une voie d’amélioration des performances et du réalisme des modèles pluie-débit?, Ph.D. thesis, UPMC, Cemagref, https://hal.inrae.fr/tel-02591478 (last access: 20 October 2025), 2008. a
Leduc, M., Mailhot, A., Frigon, A., Martel, J.-L., Ludwig, R., Brietzke, G. B., Giguère, M., Brissette, F., Turcotte, R., Braun, M., and Scinocca, J.: The ClimEx Project: A 50-Member Ensemble of Climate Change Projections at 12-km Resolution over Europe and Northeastern North America with the Canadian Regional Climate Model (CRCM5), Journal of Applied Meteorology and Climatology, 58, 663–693, https://doi.org/10.1175/jamc-d-18-0021.1, 2019. a, b
Lehner, F. and Deser, C.: Origin, importance, and predictive limits of internal climate variability, Environmental Research: Climate, 2, 023001, https://doi.org/10.1088/2752-5295/accf30, 2023. a
Lehner, F., Deser, C., Maher, N., Marotzke, J., Fischer, E. M., Brunner, L., Knutti, R., and Hawkins, E.: Partitioning climate projection uncertainty with multiple large ensembles and CMIP5/6, Earth Syst. Dynam., 11, 491–508, https://doi.org/10.5194/esd-11-491-2020, 2020. a, b
Li, C., Sinha, E., Horton, D. E., Diffenbaugh, N. S., and Michalak, A. M.: Joint bias correction of temperature and precipitation in climate model simulations, Journal of Geophysical Research: Atmospheres, 119, https://doi.org/10.1002/2014jd022514, 2014. a
Madsen, H., Lawrence, D., Lang, M., Martinkova, M., and Kjeldsen, T.: Review of trend analysis and climate change projections of extreme precipitation and floods in Europe, Journal of Hydrology, 519, 3634–3650, https://doi.org/10.1016/j.jhydrol.2014.11.003, 2014. a
Maher, N., Milinski, S., and Ludwig, R.: Large ensemble climate model simulations: introduction, overview, and future prospects for utilising multiple types of large ensemble, Earth Syst. Dynam., 12, 401–418, https://doi.org/10.5194/esd-12-401-2021, 2021. a, b
Maher, P., Vallis, G. K., Sherwood, S. C., Webb, M. J., and Sansom, P. G.: The Impact of Parameterized Convection on Climatological Precipitation in Atmospheric Global Climate Models, Geophysical Research Letters, 45, 3728–3736, https://doi.org/10.1002/2017gl076826, 2018. a
Maraun, D.: Bias Correction, Quantile Mapping, and Downscaling: Revisiting the Inflation Issue, Journal of Climate, 26, 2137–2143, https://doi.org/10.1175/jcli-d-12-00821.1, 2013. a, b, c
Maraun, D.: Bias Correcting Climate Change Simulations – a Critical Review, Current Climate Change Reports, 2, 211–220, https://doi.org/10.1007/s40641-016-0050-x, 2016. a
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, Nature Climate Change, 7, 764–773, https://doi.org/10.1038/nclimate3418, 2017. a
Martynov, A., Laprise, R., Sushama, L., Winger, K., Šeparović, L., and Dugas, B.: Reanalysis-driven climate simulation over CORDEX North America domain using the Canadian Regional Climate Model, version 5: model performance evaluation, Climate Dynamics, 41, 2973–3005, https://doi.org/10.1007/s00382-013-1778-9, 2013. a
Matiu, M., Napoli, A., Kotlarski, S., Zardi, D., Bellin, A., and Majone, B.: Elevation-dependent biases of raw and bias-adjusted EURO-CORDEX regional climate models in the European Alps, Climate Dynamics, https://doi.org/10.1007/s00382-024-07376-y, 2024. a, b
Meinshausen, M., Smith, S. J., Calvin, K., Daniel, J. S., Kainuma, M. L. T., Lamarque, J.-F., Matsumoto, K., Montzka, S. A., Raper, S. C. B., Riahi, K., Thomson, A., Velders, G. J. M., and van Vuuren, D. P.: The RCP greenhouse gas concentrations and their extensions from 1765 to 2300, Climatic Change, 109, 213–241, https://doi.org/10.1007/s10584-011-0156-z, 2011. a
MeteoSwiss: RhiresD – Daily precipitation (final analysis, 1961 – last month), https://www.meteoswiss.admin.ch/home/climate/swiss-climate-in-detail/raeumliche-klimaanalysen.html (last access: 19 July 2024), 2019a. a
MeteoSwiss: TabsD - Daily mean temperature (1961 – present), https://www.meteoswiss.admin.ch/home/climate/swiss-climate-in-detail/raeumliche-klimaanalysen.html (last access: 19 July 2024), 2019b. a
Michelangeli, P., Vrac, M., and Loukos, H.: Probabilistic downscaling approaches: Application to wind cumulative distribution functions, Geophysical Research Letters, 36, https://doi.org/10.1029/2009gl038401, 2009. a, b, c
Milinski, S., Maher, N., and Olonscheck, D.: How large does a large ensemble need to be?, Earth Syst. Dynam., 11, 885–901, https://doi.org/10.5194/esd-11-885-2020, 2020. a
Muelchi, R., Rössler, O., Schwanbeck, J., Weingartner, R., and Martius, O.: River runoff in Switzerland in a changing climate – changes in moderate extremes and their seasonality, Hydrol. Earth Syst. Sci., 25, 3577–3594, https://doi.org/10.5194/hess-25-3577-2021, 2021a. a
Muelchi, R., Rössler, O., Schwanbeck, J., Weingartner, R., and Martius, O.: River runoff in Switzerland in a changing climate – runoff regime changes and their time of emergence, Hydrol. Earth Syst. Sci., 25, 3071–3086, https://doi.org/10.5194/hess-25-3071-2021, 2021b. a
Muerth, M. J., Gauvin St-Denis, B., Ricard, S., Velázquez, J. A., Schmid, J., Minville, M., Caya, D., Chaumont, D., Ludwig, R., and Turcotte, R.: On the need for bias correction in regional climate scenarios to assess climate change impacts on river runoff, Hydrol. Earth Syst. Sci., 17, 1189–1204, https://doi.org/10.5194/hess-17-1189-2013, 2013. a
Oudin, L., Hervieu, F., Michel, C., Perrin, C., Andréassian, V., Anctil, F., and Loumagne, C.: Which potential evapotranspiration input for a lumped rainfall–runoff model? Part 2: Towards a simple and efficient potential evapotranspiration model for rainfall–runoff modelling, Journal of Hydrology, 303, 290–306, https://doi.org/10.1016/j.jhydrol.2004.08.026, 2005. a
Parajka, J., Merz, R., and Blöschl, G.: Uncertainty and multiple objective calibration in regional water balance modelling: case study in 320 Austrian catchments, Hydrological Processes, 21, 435–446, https://doi.org/10.1002/hyp.6253, 2007. a, b
Pastén-Zapata, E., Jones, J. M., Moggridge, H., and Widmann, M.: Evaluation of the performance of Euro-CORDEX Regional Climate Models for assessing hydrological climate change impacts in Great Britain: A comparison of different spatial resolutions and quantile mapping bias correction methods, Journal of Hydrology, 584, 124653, https://doi.org/10.1016/j.jhydrol.2020.124653, 2020. a, b
R Core Team: R: A Language and Environment for Statistical Computing, R Foundation for Statistical Computing, Vienna, Austria, https://www.R-project.org/ (last access: 20 October 2025), 2022. a
Rajczak, J., Kotlarski, S., Salzmann, N., and Schär, C.: Robust climate scenarios for sites with sparse observations: a two‐step bias correction approach, International Journal of Climatology, 36, 1226–1243, https://doi.org/10.1002/joc.4417, 2015. a, b
Robin, Y. and Vrac, M.: Is time a variable like the others in multivariate statistical downscaling and bias correction?, Earth Syst. Dynam., 12, 1253–1273, https://doi.org/10.5194/esd-12-1253-2021, 2021. a
Robin, Y., Vrac, M., Naveau, P., and Yiou, P.: Multivariate stochastic bias corrections with optimal transport, Hydrol. Earth Syst. Sci., 23, 773–786, https://doi.org/10.5194/hess-23-773-2019, 2019. a, b
Rolls, R. J., Leigh, C., and Sheldon, F.: Mechanistic effects of low-flow hydrology on riverine ecosystems: ecological principles and consequences of alteration, Freshwater Science, 31, 1163–1186, https://doi.org/10.1899/12-002.1, 2012. a
Schulz, K. and Bernhardt, M.: The end of trend estimation for extreme floods under climate change?: Invited Commentaries, Hydrological Processes, 30, 1804–1808, https://doi.org/10.1002/hyp.10816, 2016. a, b
Suarez-Gutierrez, L., Milinski, S., and Maher, N.: Exploiting large ensembles for a better yet simpler climate model evaluation, Climate Dynamics, 57, 2557–2580, https://doi.org/10.1007/s00382-021-05821-w, 2021. a, b, c, d
Sørland, S. L., Fischer, A. M., Kotlarski, S., Künsch, H. R., Liniger, M. A., Rajczak, J., Schär, C., Spirig, C., Strassmann, K., and Knutti, R.: CH2018 – National climate scenarios for Switzerland: How to construct consistent multi-model projections from ensembles of opportunity, Climate Services, 20, 100196, https://doi.org/10.1016/j.cliser.2020.100196, 2020. a
Teutschbein, C. and Seibert, J.: Bias correction of regional climate model simulations for hydrological climate-change impact studies: Review and evaluation of different methods, Journal of Hydrology, 456–457, 12–29, https://doi.org/10.1016/j.jhydrol.2012.05.052, 2012. a, b
Teutschbein, C., Wetterhall, F., and Seibert, J.: Evaluation of different downscaling techniques for hydrological climate-change impact studies at the catchment scale, Climate Dynamics, 37, 2087–2105, https://doi.org/10.1007/s00382-010-0979-8, 2011. a
Tootoonchi, F., Haerter, J. O., Todorović, A., Räty, O., Grabs, T., and Teutschbein, C.: Uni- and multivariate bias adjustment methods in Nordic catchments: Complexity and performance in a changing climate, Science of The Total Environment, 853, 158615, https://doi.org/10.1016/j.scitotenv.2022.158615, 2022. a
Tootoonchi, F., Todorović, A., Grabs, T., and Teutschbein, C.: Uni- and multivariate bias adjustment of climate model simulations in Nordic catchments: Effects on hydrological signatures relevant for water resources management in a changing climate, Journal of Hydrology, 623, 129807, https://doi.org/10.1016/j.jhydrol.2023.129807, 2023. a, b, c, d, e, f
Valéry, A., Andréassian, V., and Perrin, C.: “As simple as possible but not simpler”: What is useful in a temperature-based snow-accounting routine? Part 2 – Sensitivity analysis of the Cemaneige snow accounting routine on 380 catchments, Journal of Hydrology, 517, 1176–1187, https://doi.org/10.1016/j.jhydrol.2014.04.058, 2014. a
van der Wiel, K., Wanders, N., Selten, F. M., and Bierkens, M. F. P.: Added Value of Large Ensemble Simulations for Assessing Extreme River Discharge in a 2 °C Warmer World, Geophysical Research Letters, 46, 2093–2102, https://doi.org/10.1029/2019gl081967, 2019. a, b
Van Loon, A. F.: Hydrological drought explained, WIREs Water, 2, 359–392, https://doi.org/10.1002/wat2.1085, 2015. a
von Trentini, F., Aalbers, E. E., Fischer, E. M., and Ludwig, R.: Comparing interannual variability in three regional single-model initial-condition large ensembles (SMILEs) over Europe, Earth Syst. Dynam., 11, 1013–1031, https://doi.org/10.5194/esd-11-1013-2020, 2020. a
Vrac, M.: Multivariate bias adjustment of high-dimensional climate simulations: the Rank Resampling for Distributions and Dependences (R2D2) bias correction, Hydrol. Earth Syst. Sci., 22, 3175–3196, https://doi.org/10.5194/hess-22-3175-2018, 2018. a, b
Vrac, M. and Thao, S.: R2D2 v2.0: accounting for temporal dependences in multivariate bias correction via analogue rank resampling, Geosci. Model Dev., 13, 5367–5387, https://doi.org/10.5194/gmd-13-5367-2020, 2020. a, b
Vrac, M., Drobinski, P., Merlo, A., Herrmann, M., Lavaysse, C., Li, L., and Somot, S.: Dynamical and statistical downscaling of the French Mediterranean climate: uncertainty assessment, Nat. Hazards Earth Syst. Sci., 12, 2769–2784, https://doi.org/10.5194/nhess-12-2769-2012, 2012. a, b, c
Vrac, M., Noël, T., and Vautard, R.: Bias correction of precipitation through Singularity Stochastic Removal: Because occurrences matter, Journal of Geophysical Research: Atmospheres, 121, 5237–5258, https://doi.org/10.1002/2015jd024511, 2016. a, b
Wilcoxon, F.: Individual Comparisons by Ranking Methods, Biometrics Bulletin, 1, 80, https://doi.org/10.2307/3001968, 1945. a, b
Willkofer, F., Wood, R. R., and Ludwig, R.: Assessing the impact of climate change on high return levels of peak flows in Bavaria applying the CRCM5 large ensemble, Hydrol. Earth Syst. Sci., 28, 2969–2989, https://doi.org/10.5194/hess-28-2969-2024, 2024. a, b, c
Wood, R. R. and Ludwig, R.: Analyzing Internal Variability and Forced Response of Subdaily and Daily Extreme Precipitation Over Europe, Geophysical Research Letters, 47, https://doi.org/10.1029/2020gl089300, 2020. a, b, c
Wood, R. R., Lehner, F., Pendergrass, A. G., and Schlunegger, S.: Changes in precipitation variability across time scales in multiple global climate model large ensembles, Environmental Research Letters, 16, 084022, https://doi.org/10.1088/1748-9326/ac10dd, 2021. a, b
Šeparović, L., Alexandru, A., Laprise, R., Martynov, A., Sushama, L., Winger, K., Tete, K., and Valin, M.: Present climate and climate change over North America as simulated by the fifth-generation Canadian regional climate model, Climate Dynamics, 41, 3167–3201, https://doi.org/10.1007/s00382-013-1737-5, 2013. a
Short summary
To study floods and droughts that are likely to change in the future, we use climate projections from climate models. However, we first need to adjust the systematic biases of these projections at the catchment scale before using them in hydrological models. Our study compares statistical methods that can adjust these biases but specifically for climate projections that enable a quantification of internal climate variability. We provide recommendations on the most appropriate methods.
To study floods and droughts that are likely to change in the future, we use climate projections...