Articles | Volume 24, issue 4
https://doi.org/10.5194/nhess-24-1261-2024
© Author(s) 2024. 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-24-1261-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
10 Apr 2024
Research article |
| 10 Apr 2024
| 10 Apr 2024
Interannual variations in the seasonal cycle of extreme precipitation in Germany and the response to climate change
Madlen Peter
CORRESPONDING AUTHOR
Institute of Meteorology, Freie Universität Berlin, Carl-Heinrich-Becker-Weg 6–10, 12165 Berlin, Germany
Henning W. Rust
Institute of Meteorology, Freie Universität Berlin, Carl-Heinrich-Becker-Weg 6–10, 12165 Berlin, Germany
Uwe Ulbrich
Institute of Meteorology, Freie Universität Berlin, Carl-Heinrich-Becker-Weg 6–10, 12165 Berlin, Germany
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Andreas Trojand, Henning W. Rust, and Uwe Ulbrich
Nat. Hazards Earth Syst. Sci., 25, 2331–2350, https://doi.org/10.5194/nhess-25-2331-2025, https://doi.org/10.5194/nhess-25-2331-2025, 2025
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The study investigates how the intensity of previous windstorm events and the time between two events affect the vulnerability of residential buildings in Germany. By analyzing 23 years of data, it was found that higher intensity of previous events generally reduces vulnerability in subsequent storms, while shorter intervals between events increase vulnerability. The results emphasize the approach of considering vulnerability in risk assessments as temporally dynamic.
Rike Lorenz, Nico Becker, Barry Gardiner, Uwe Ulbrich, Marc Hanewinkel, and Benjamin Schmitz
Nat. Hazards Earth Syst. Sci., 25, 2179–2196, https://doi.org/10.5194/nhess-25-2179-2025, https://doi.org/10.5194/nhess-25-2179-2025, 2025
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Tree fall events have an impact on forests and transport systems. Our study explored tree fall in relation to wind and other weather conditions. We used tree fall data along railway lines and ERA5 and radar meteorological data to build a logistic regression model. We found that high and prolonged wind speeds, wet conditions, and high air density increase tree fall risk. These factors might change in the changing climate, which in return will change risks for trees, forests and transport.
Yan Li, Bo Huang, Chunping Tan, Xia Zhang, Francesco Cherubini, and Henning W. Rust
Hydrol. Earth Syst. Sci., 29, 1637–1658, https://doi.org/10.5194/hess-29-1637-2025, https://doi.org/10.5194/hess-29-1637-2025, 2025
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Deforestation has a significant impact on climate, yet its effects on drought remain less understood. This study investigates how deforestation affects drought across various climate zones and timescales. Findings indicate that deforestation leads to drier conditions in tropical regions and wetter conditions in arid areas, with minimal effects in temperate zones. Long-term drought is more affected than short-term drought, offering valuable insights into vegetation–climate interactions.
Franziska Tügel, Katrin M. Nissen, Lennart Steffen, Yangwei Zhang, Uwe Ulbrich, and Reinhard Hinkelmann
EGUsphere, https://doi.org/10.5194/egusphere-2025-445, https://doi.org/10.5194/egusphere-2025-445, 2025
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This study examines how extreme rainfall in Berlin, Germany, may intensify due to global warming and how that could worsen flooding in a selected part of the city. We assess the role of the drainage system, infiltration from unsealed surfaces, and a potential adaptation scenario with all roofs as retention roofs in reducing flooding under extreme rainfall. Combining climate and hydrodynamic simulations, we provide insights into future challenges and possible solutions for urban flood management.
Andy Richling, Jens Grieger, and Henning W. Rust
Geosci. Model Dev., 18, 361–375, https://doi.org/10.5194/gmd-18-361-2025, https://doi.org/10.5194/gmd-18-361-2025, 2025
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The performance of weather and climate prediction systems is variable in time and space. It is of interest how this performance varies in different situations. We provide a decomposition of a skill score (a measure of forecast performance) as a tool for detailed assessment of performance variability to support model development or forecast improvement. The framework is exemplified with decadal forecasts to assess the impact of different ocean states in the North Atlantic on temperature forecast.
Bjorn Stevens, Stefan Adami, Tariq Ali, Hartwig Anzt, Zafer Aslan, Sabine Attinger, Jaana Bäck, Johanna Baehr, Peter Bauer, Natacha Bernier, Bob Bishop, Hendryk Bockelmann, Sandrine Bony, Guy Brasseur, David N. Bresch, Sean Breyer, Gilbert Brunet, Pier Luigi Buttigieg, Junji Cao, Christelle Castet, Yafang Cheng, Ayantika Dey Choudhury, Deborah Coen, Susanne Crewell, Atish Dabholkar, Qing Dai, Francisco Doblas-Reyes, Dale Durran, Ayoub El Gaidi, Charlie Ewen, Eleftheria Exarchou, Veronika Eyring, Florencia Falkinhoff, David Farrell, Piers M. Forster, Ariane Frassoni, Claudia Frauen, Oliver Fuhrer, Shahzad Gani, Edwin Gerber, Debra Goldfarb, Jens Grieger, Nicolas Gruber, Wilco Hazeleger, Rolf Herken, Chris Hewitt, Torsten Hoefler, Huang-Hsiung Hsu, Daniela Jacob, Alexandra Jahn, Christian Jakob, Thomas Jung, Christopher Kadow, In-Sik Kang, Sarah Kang, Karthik Kashinath, Katharina Kleinen-von Königslöw, Daniel Klocke, Uta Kloenne, Milan Klöwer, Chihiro Kodama, Stefan Kollet, Tobias Kölling, Jenni Kontkanen, Steve Kopp, Michal Koran, Markku Kulmala, Hanna Lappalainen, Fakhria Latifi, Bryan Lawrence, June Yi Lee, Quentin Lejeun, Christian Lessig, Chao Li, Thomas Lippert, Jürg Luterbacher, Pekka Manninen, Jochem Marotzke, Satoshi Matsouoka, Charlotte Merchant, Peter Messmer, Gero Michel, Kristel Michielsen, Tomoki Miyakawa, Jens Müller, Ramsha Munir, Sandeep Narayanasetti, Ousmane Ndiaye, Carlos Nobre, Achim Oberg, Riko Oki, Tuba Özkan-Haller, Tim Palmer, Stan Posey, Andreas Prein, Odessa Primus, Mike Pritchard, Julie Pullen, Dian Putrasahan, Johannes Quaas, Krishnan Raghavan, Venkatachalam Ramaswamy, Markus Rapp, Florian Rauser, Markus Reichstein, Aromar Revi, Sonakshi Saluja, Masaki Satoh, Vera Schemann, Sebastian Schemm, Christina Schnadt Poberaj, Thomas Schulthess, Cath Senior, Jagadish Shukla, Manmeet Singh, Julia Slingo, Adam Sobel, Silvina Solman, Jenna Spitzer, Philip Stier, Thomas Stocker, Sarah Strock, Hang Su, Petteri Taalas, John Taylor, Susann Tegtmeier, Georg Teutsch, Adrian Tompkins, Uwe Ulbrich, Pier-Luigi Vidale, Chien-Ming Wu, Hao Xu, Najibullah Zaki, Laure Zanna, Tianjun Zhou, and Florian Ziemen
Earth Syst. Sci. Data, 16, 2113–2122, https://doi.org/10.5194/essd-16-2113-2024, https://doi.org/10.5194/essd-16-2113-2024, 2024
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To manage Earth in the Anthropocene, new tools, new institutions, and new forms of international cooperation will be required. Earth Virtualization Engines is proposed as an international federation of centers of excellence to empower all people to respond to the immense and urgent challenges posed by climate change.
Yan Li, Bo Huang, and Henning W. Rust
Hydrol. Earth Syst. Sci., 28, 321–339, https://doi.org/10.5194/hess-28-321-2024, https://doi.org/10.5194/hess-28-321-2024, 2024
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The inconsistent changes in temperature and precipitation induced by forest cover change are very likely to affect drought condition. We use a set of statistical models to explore the relationship between forest cover change and drought change in different timescales and climate zones. We find that the influence of forest cover on droughts varies under different precipitation and temperature quantiles. Forest cover also could modulate the impacts of precipitation and temperature on drought.
Katrin M. Nissen, Martina Wilde, Thomas M. Kreuzer, Annika Wohlers, Bodo Damm, and Uwe Ulbrich
Nat. Hazards Earth Syst. Sci., 23, 2737–2748, https://doi.org/10.5194/nhess-23-2737-2023, https://doi.org/10.5194/nhess-23-2737-2023, 2023
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The effect of climate change on rockfall probability in the German low mountain regions is investigated in observations and in 23 different climate scenario simulations. Under a pessimistic greenhouse gas scenario, the simulations suggest a decrease in rockfall probability. This reduction is mainly caused by a decrease in the number of freeze–thaw cycles due to higher atmospheric temperatures.
Johannes Riebold, Andy Richling, Uwe Ulbrich, Henning Rust, Tido Semmler, and Dörthe Handorf
Weather Clim. Dynam., 4, 663–682, https://doi.org/10.5194/wcd-4-663-2023, https://doi.org/10.5194/wcd-4-663-2023, 2023
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Arctic sea ice loss might impact the atmospheric circulation outside the Arctic and therefore extremes over mid-latitudes. Here, we analyze model experiments to initially assess the influence of sea ice loss on occurrence frequencies of large-scale circulation patterns. Some of these detected circulation changes can be linked to changes in occurrences of European temperature extremes. Compared to future global temperature increases, the sea-ice-related impacts are however of secondary relevance.
Edmund P. Meredith, Uwe Ulbrich, and Henning W. Rust
Geosci. Model Dev., 16, 851–867, https://doi.org/10.5194/gmd-16-851-2023, https://doi.org/10.5194/gmd-16-851-2023, 2023
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Cell-tracking algorithms allow for the study of properties of a convective cell across its lifetime and, in particular, how these respond to climate change. We investigated whether the design of the algorithm can affect the magnitude of the climate-change signal. The algorithm's criteria for identifying a cell were found to have a strong impact on the warming response. The sensitivity of the warming response to different algorithm settings and cell types should thus be fully explored.
Alberto Caldas-Alvarez, Markus Augenstein, Georgy Ayzel, Klemens Barfus, Ribu Cherian, Lisa Dillenardt, Felix Fauer, Hendrik Feldmann, Maik Heistermann, Alexia Karwat, Frank Kaspar, Heidi Kreibich, Etor Emanuel Lucio-Eceiza, Edmund P. Meredith, Susanna Mohr, Deborah Niermann, Stephan Pfahl, Florian Ruff, Henning W. Rust, Lukas Schoppa, Thomas Schwitalla, Stella Steidl, Annegret H. Thieken, Jordis S. Tradowsky, Volker Wulfmeyer, and Johannes Quaas
Nat. Hazards Earth Syst. Sci., 22, 3701–3724, https://doi.org/10.5194/nhess-22-3701-2022, https://doi.org/10.5194/nhess-22-3701-2022, 2022
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In a warming climate, extreme precipitation events are becoming more frequent. To advance our knowledge on such phenomena, we present a multidisciplinary analysis of a selected case study that took place on 29 June 2017 in the Berlin metropolitan area. Our analysis provides evidence of the extremeness of the case from the atmospheric and the impacts perspectives as well as new insights on the physical mechanisms of the event at the meteorological and climate scales.
Katrin M. Nissen, Stefan Rupp, Thomas M. Kreuzer, Björn Guse, Bodo Damm, and Uwe Ulbrich
Nat. Hazards Earth Syst. Sci., 22, 2117–2130, https://doi.org/10.5194/nhess-22-2117-2022, https://doi.org/10.5194/nhess-22-2117-2022, 2022
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A statistical model is introduced which quantifies the influence of individual potential triggering factors and their interactions on rockfall probability in central Europe. The most important factor is daily precipitation, which is most effective if sub-surface moisture levels are high. Freeze–thaw cycles in the preceding days can further increase the rockfall hazard. The model can be applied to climate simulations in order to investigate the effect of climate change on rockfall probability.
Robert Polzin, Annette Müller, Henning Rust, Peter Névir, and Péter Koltai
Nonlin. Processes Geophys., 29, 37–52, https://doi.org/10.5194/npg-29-37-2022, https://doi.org/10.5194/npg-29-37-2022, 2022
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In this study, a recent algorithmic framework called Direct Bayesian Model Reduction (DBMR) is applied which provides a scalable probability-preserving identification of reduced models directly from data. The stochastic method is tested in a meteorological application towards a model reduction to latent states of smaller scale convective activity conditioned on large-scale atmospheric flow.
Noelia Otero, Oscar E. Jurado, Tim Butler, and Henning W. Rust
Atmos. Chem. Phys., 22, 1905–1919, https://doi.org/10.5194/acp-22-1905-2022, https://doi.org/10.5194/acp-22-1905-2022, 2022
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Surface ozone and temperature are strongly dependent and their extremes might be exacerbated by underlying climatological drivers, such as atmospheric blocking. Using an observational data set, we measure the dependence structure between ozone and temperature under the influence of atmospheric blocking. Blocks enhanced the probability of occurrence of compound ozone and temperature extremes over northwestern and central Europe, leading to greater health risks.
Felix S. Fauer, Jana Ulrich, Oscar E. Jurado, and Henning W. Rust
Hydrol. Earth Syst. Sci., 25, 6479–6494, https://doi.org/10.5194/hess-25-6479-2021, https://doi.org/10.5194/hess-25-6479-2021, 2021
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Extreme rainfall events are modeled in this study for different timescales. A new parameterization of the dependence between extreme values and their timescale enables our model to estimate extremes on very short (1 min) and long (5 d) timescales simultaneously. We compare different approaches of modeling this dependence and find that our new model improves performance for timescales between 2 h and 2 d without affecting model performance on other timescales.
Jana Ulrich, Felix S. Fauer, and Henning W. Rust
Hydrol. Earth Syst. Sci., 25, 6133–6149, https://doi.org/10.5194/hess-25-6133-2021, https://doi.org/10.5194/hess-25-6133-2021, 2021
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The characteristics of extreme precipitation on different timescales as well as in different seasons are relevant information, e.g., for designing hydrological structures or managing water supplies. Therefore, our aim is to describe these characteristics simultaneously within one model. We find similar characteristics for short extreme precipitation at all considered stations in Germany but pronounced regional differences with respect to the seasonality of long-lasting extreme events.
Carola Detring, Annette Müller, Lisa Schielicke, Peter Névir, and Henning W. Rust
Weather Clim. Dynam., 2, 927–952, https://doi.org/10.5194/wcd-2-927-2021, https://doi.org/10.5194/wcd-2-927-2021, 2021
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Stationary, long-lasting blocked weather patterns can lead to extreme conditions. Within this study the temporal evolution of the occurrence probability is analyzed, and the onset, decay and transition probabilities of blocking within the past 30 years are modeled. Using Markov models combined with logistic regression, we found large changes in summer, where the probability of transitions to so-called Omega blocks increases strongly, while the unblocked state becomes less probable.
Alexander Pasternack, Jens Grieger, Henning W. Rust, and Uwe Ulbrich
Geosci. Model Dev., 14, 4335–4355, https://doi.org/10.5194/gmd-14-4335-2021, https://doi.org/10.5194/gmd-14-4335-2021, 2021
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Decadal climate ensemble forecasts are increasingly being used to guide adaptation measures. To ensure the applicability of these probabilistic predictions, inherent systematic errors of the prediction system must be adjusted. Since it is not clear which statistical model is optimal for this purpose, we propose a recalibration strategy with a systematic model selection based on non-homogeneous boosting for identifying the most relevant features for both ensemble mean and ensemble spread.
Nico Becker, Henning W. Rust, and Uwe Ulbrich
Nat. Hazards Earth Syst. Sci., 20, 2857–2871, https://doi.org/10.5194/nhess-20-2857-2020, https://doi.org/10.5194/nhess-20-2857-2020, 2020
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A set of models is developed to forecast hourly probabilities of weather-related road accidents in Germany at the spatial scale of administrative districts. Model verification shows that using precipitation and temperature data leads to the best accident forecasts. Based on weather forecast data we show that skilful predictions of accident probabilities of up to 21 h ahead are possible. The models can be used to issue impact-based warnings, which are relevant for road users and authorities.
Cited articles
Aleshina, M., Semenov, V., and Chernokulsky, A.: A link between surface air temperature and extreme precipitation over Russia from station and reanalysis data, Environ. Res. Lett., 16, 105004, https://doi.org/10.1088/1748-9326/ac1cba, 2021. a
Ambrosino, C., Chandler, R. E., and Todd, M. C.: Southern African monthly rainfall variability: An analysis based on generalized linear models, J. Climate, 24, 4600–4617, 2011. a
Arun, A., Haque, M. M., Lyon, C., Sayed, T., Washington, S., Loewenherz, F., Akers, D., Bandy, M., Bahl, V., Ananthanarayanan, G., and Shu, Y.: Leading Pedestrian Intervals – Yay or Nay? A Before-After Evaluation using Traffic Conflict-Based Peak Over Threshold Approach, https://bellevuewa.gov/sites/default/files/media/pdf_document/2022/leading-pedestrian-intervals-research-paper-010322.pdf (last access: 18 March 2024), 2022. a
Bahrami, M. and Mahmoudi, M. R.: Long-term temporal trend analysis of climatic parameters using polynomial regression analysis over the Fasa Plain, southern Iran, Meteorol. Atmos. Phys., 134, 1–12, 2022. a
Bentzien, S. and Friederichs, P.: Decomposition and graphical portrayal of the quantile score, Q. J. Roy. Meteor. Soc., 140, 1924–1934, 2014. a
Botzen, W. J. W., Aerts, J., and van den Bergh, J. C. J. M.: Dependence of flood risk perceptions on socioeconomic and objective risk factors, Water Resour. Res., 45, W10440, https://doi.org/10.1029/2009WR007743, 2009. a
Bousquet, N. and Bernardara, P.: Extreme Value Theory with Applications to Natural Hazards: From Statistical Theory to Industrial Practice, Springer Nature, https://doi.org/10.1007/978-3-030-74942-2, 2021. a
Byun, K. and Hamlet, A. F.: A risk-based analytical framework for quantifying non-stationary flood risks and establishing infrastructure design standards in a changing environment, J. Hydrol., 584, 124575, https://doi.org/10.1016/j.jhydrol.2020.124575, 2020. a
Casanueva, A., Rodríguez-Puebla, C., Frías, M. D., and González-Reviriego, N.: Variability of extreme precipitation over Europe and its relationships with teleconnection patterns, Hydrol. Earth Syst. Sci., 18, 709–725, https://doi.org/10.5194/hess-18-709-2014, 2014. a
Davini, P. and d’Andrea, F.: From CMIP3 to CMIP6: Northern Hemisphere atmospheric blocking simulation in present and future climate, J. Climate, 33, 10021–10038, 2020. a
Derbile, E. K. and Kasei, R. A.: Vulnerability of crop production to heavy precipitation in north-eastern Ghana, Int. J. Clim. Chang. Str., 4, 36–53, 2012. a
Detring, C., Müller, A., Schielicke, L., Névir, P., and Rust, H. W.: Occurrence and transition probabilities of omega and high-over-low blocking in the Euro-Atlantic region, Weather Clim. Dynam., 2, 927–952, https://doi.org/10.5194/wcd-2-927-2021, 2021. a
DWD: KOSTRA-DWD-2000, Starkniederschlagshöhen für Deutschland–Grundlagenbericht, https://www.dwd.de/DE/fachnutzer/wasserwirtschaft/kooperationen/kostra/fortschreibung_pdf.pdf?__blob=publicationFile&v=3 (last access: 18 March 2024), 2000. a
DWD: Historical daily precipitation observations for Germany, Climate Data Center, v21.3 edn., https://opendata.dwd.de/climate_environment/CDC/observations_germany/climate/daily/more_precip/historical/DESCRIPTION_obsgermany_climate_daily_more_precip_historical_en.pdf (last access: 18 March 2024), 2021. a, b
DWD: Open-Data-Server, https://opendata.dwd.de/ (last access: 19 August 2022), 2022. a
DWD: Historical daily precipitation observations for Germany, Climate Data Center [data set], https://opendata.dwd.de/climate_environment/CDC/observations_germany/climate/daily/more_precip/historical/ (last access: 1 April 2024), 2023. a
DWD: KOSTRA DWD, https://www.dwd.de/DE/leistungen/kostra_dwd_rasterwerte/kostra_dwd_rasterwerte.html (last access: 26 January 2024), 2024. a
Embrechts, P., Klüppelberger, C., and Mikosch, T.: Modelling Extremal Events for Insurance and Fincance, Springer, Berlin, ISBN 3-540-60931-8, 1997. a
Fabozzi, F. J., Focardi, S. M., Rachev, S. T., and Arshanapalli, B. G.: The basics of financial econometrics: Tools, concepts, and asset management applications, John Wiley & Sons, ISBN 978-1-118-72743-0, 2014. a
Fauer, F. S. and Rust, H. W.: Non-stationary large-scale statistics of precipitation extremes in central Europe, Stoch. Environ. Res. Risk A., 37, 4417–4429, 2023. a
Fauer, F. S., Ulrich, J., Jurado, O. E., and Rust, H. W.: Flexible and consistent quantile estimation for intensity–duration–frequency curves, Hydrol. Earth Syst. Sci., 25, 6479–6494, https://doi.org/10.5194/hess-25-6479-2021, 2021. a
Ferreira, A., Friederichs, P., de Haan, L., Neves, C., and Schlather, M.: Estimating space-time trend and dependence of heavy rainfall, arXiv [preprint], https://doi.org/10.48550/arXiv.1707.04434, 2017. a
Friederichs, P. and Hense, A.: Statistical downscaling of extreme precipitation events using censored quantile regression, Mon. Weather Rev., 135, 2365–2378, 2007. a
Galiatsatou, P. and Prinos, P.: Analysing the effects of climate change on wave height extremes in the Greek Seas, in: ICHE 2014, Proceedings of the 11th International Conference on Hydroscience & Engineering, Hamburg, Germany, Karlsruhe: Bundesanstalt für Wasserbau, 28 September–2 October 2014, https://henry.baw.de/server/api/core/bitstreams/f661f66a-6e72-4f28-9374-ea41bca97759/content (last access: 18 March 2024), 773–782, 2014. a
Gilli, M. and Këllezi, E.: An application of extreme value theory for measuring financial risk, Computat. Econ., 27, 207–228, 2006. a
Gkillas, K. and Katsiampa, P.: An application of extreme value theory to cryptocurrencies, Econ. Lett., 164, 109–111, 2018. a
Jurado, O. E., Oesting, M., and Rust, H. W.: Implications of modeling seasonal differences in the extremal dependence of rainfall maxima, Stoch. Environ. Res. Risk A., 37, 1963–1981, https://doi.org/10.1007/s00477-022-02375-z, 2022. a
Katz, R. W., Parlange, M. B., and Naveau, P.: Statistics of extremes in hydrology, Adv. Water Resour., 25, 1287–1304, 2002. a
Kjesbu, O., Witthames, P., Solemdal, P., and Walker, M. G.: Temporal variations in the fecundity of Arcto-Norwegian cod (Gadus morhua) in response to natural changes in food and temperature, J. Sea Res., 40, 303–321, 1998. a
Maraun, D., Rust, H. W., and Osborn, T. J.: The influence of synoptic airflow on UK daily precipitation extremes. Part I: observed spatio-temporal relations, Clim. Dynam., 36, 261–275, 2011. a
Méndez, F. J., Menéndez, M., Luceño, A., and Losada, I. J.: Analyzing monthly extreme sea levels with a time-dependent GEV model, J. Atmos. Ocean. Tech., 24, 894–911, 2007. a
Min, J. L. J. and Halim, S. A.: Rainfall Modelling using Generalized Extreme Value Distribution with Cyclic Covariate, Mathematics and Statistics, 8, 762–772, https://doi.org/10.13189/ms.2020.080617, 2020. a
Moghaddasi, M., Anvari, S., and Mohammadi, T.: Comparison of extreme value theory approaches in temperature frequency analysis (case study: Arak plain in Iran), Arab. J. Geosci., 15, 1–13, 2022. a
Mondal, A. and Daniel, D.: Return levels under nonstationarity: The need to update infrastructure design strategies, J. Hydrol. Eng., 24, 04018060, https://doi.org/10.1061/(ASCE)HE.1943-5584.0001738, 2019. a
Mudelsee, M.: Trend analysis of climate time series: A review of methods, Earth-Sci. Rev., 190, 310–322, 2019. a
Naveau, P., Nogaj, M., Ammann, C., Yiou, P., Cooley, D., and Jomelli, V.: Statistical methods for the analysis of climate extremes, C. R. Geosci., 337, 1013–1022, 2005. a
Neath, A. A. and Cavanaugh, J. E.: The Bayesian information criterion: background, derivation, and applications, Wires Comput. Mol. Sci., 4, 199–203, 2012. a
Pinto, J. G., Ulbrich, U., Leckebusch, G. C., Spangehl, T., Reyers, M., and Zacharias, S.: Changes in storm track and cyclone activity in three SRES ensemble experiments with the ECHAM5/MPI-OM1 GCM, Clim. Dynam., 29, 195–210, 2007. a
Pinto, J. G., Zacharias, S., Fink, A. H., Leckebusch, G. C., and Ulbrich, U.: Factors contributing to the development of extreme North Atlantic cyclones and their relationship with the NAO, Clim. Dynam., 32, 711–737, 2009. a
Pörtner, H.-O., Roberts, D. C., Tignor, M., Poloczanska, E. S., Mintenbeck, K., Alegría, A., Craig, M., Langsdorf, S., Löschke, S., Möller, V., Okem, A., and Rama, B. (Eds.): Climate change 2022: Impacts, adaptation and vulnerability, Cambridge University Press, Cambridge, UK and New York, NY, USA, https://doi.org/10.1017/9781009325844, 2022. a, b
Priestley, M. B.: Spectral Analysis and Time Series, Academic Press, London, ISBN 0-12-564922-3, 1992. a
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: 18 March 2024), 2022. a
Ragulina, G. and Reitan, T.: Generalized extreme value shape parameter and its nature for extreme precipitation using long time series and the Bayesian approach, Hydrolog. Sci. J., 62, 863–879, 2017. a
Rajczak, J., Pall, P., and Schär, C.: Projections of extreme precipitation events in regional climate simulations for Europe and the Alpine Region, J. Geophys. Res.-Atmos., 118, 3610–3626, 2013. a
Ribereau, P., Naveau, P., and Guillou, A.: A note of caution when interpreting parameters of the distribution of excesses, Adv. Water Resour., 34, 1215–1221, https://doi.org/10.1016/j.advwatres.2011.05.003, 2011. a
Richling, A., Grieger, J., and Rust, H. W.: Decomposition of skill scores for conditional verification – Impact of AMO phases on the predictability of decadal temperature forecasts, EGUsphere [preprint], https://doi.org/10.5194/egusphere-2023-2582, 2024. a
Rosenzweig, C., Tubiello, F. N., Goldberg, R., Mills, E., and Bloomfield, J.: Increased crop damage in the US from excess precipitation under climate change, Global Environ. Chang., 12, 197–202, 2002. a
Rust, H. W.: The Effect of Long-Range Dependence on Modelling Extremes with the Generalised Extreme Value Distribution, Eur. Phys. J.-Spec. Top., 174, 91–97, 2009. a
Shacham, M. and Brauner, N.: Minimizing the effects of collinearity in polynomial regression, Ind. Eng. Chem. Res., 36, 4405–4412, 1997. a
Szigeti, M., Ferenci, T., and Kovács, L.: The use of peak over threshold methods to characterise blood glucose curves, in: 2020 IEEE 14th International Symposium on Applied Computational Intelligence and Informatics (SACI), IEEE, Timişoara, Romania, 21–23 May 2020, 000199–000204, https://real.mtak.hu/121217/1/szigeti2020SACI.pdf (last access: 20 March 2024), 2020. a
Teegavarapu, R. S. V.: Floods in a changing climate: extreme precipitation, Cambridge University Press, ISBN 978-1-107-01878-5, 2012. a
Thomson, R., Drynan, L., and Veldema, A.: Australian Rainfall and Runoff Revision Project 20: Risk Assessment and Design Life, https://arr.ga.gov.au/__data/assets/pdf_file/0003/40548/ARR_Project_20_Stage3_Report.pdf (last access: 20 March 2024), 2015. a
Trenberth, K. E., Dai, A., Rasmussen, R. M., and Parsons, D. B.: The changing character of precipitation, B. Am. Meteorol. Soc., 84, 1205–1218, 2003. a
Trenczek, J., Lühr, O., Eiserbeck, L., Sandhövel, M., and Leuschner, V.: Übersicht vergangener Extremwetterschäden in Deutschland, https://www.prognos.com/de/projekt/bezifferung-von-klimafolgekosten-deutschland (last access: 20 March 2024) 2022. a
Ulrich, J., Jurado, O. E., Peter, M., Scheibel, M., and Rust, H. W.: Estimating IDF curves consistently over durations with spatial covariates, Water, 12, 3119, https://doi.org/10.3390/w12113119, 2020. a, b, c
Ulrich, J., Fauer, F. S., and Rust, H. W.: Modeling seasonal variations of extreme rainfall on different timescales in Germany, Hydrol. Earth Syst. Sci., 25, 6133–6149, https://doi.org/10.5194/hess-25-6133-2021, 2021. a, b, c
Villafuerte, M. Q., Matsumoto, J., and Kubota, H.: Changes in extreme rainfall in the Philippines (1911–2010) linked to global mean temperature and ENSO, Int. J. Climatol., 35, 2033–2044, 2015. a
Vormoor, K., Lawrence, D., Heistermann, M., and Bronstert, A.: Climate change impacts on the seasonality and generation processes of floods – projections and uncertainties for catchments with mixed snowmelt/rainfall regimes, Hydrol. Earth Syst. Sci., 19, 913–931, https://doi.org/10.5194/hess-19-913-2015, 2015. a
Seneviratne, S. I., Zhang, X., Adnan, M., Badi, W., Dereczynski, C., Di Luca, A., Ghosh, S., Iskandar, I., Kossin, J., Lewis, S., Otto, F., Pinto, I., Satoh, M., Vicente-Serrano, S. M., Wehner, M., and Zho, B.: Weather and climate extreme events in a changing climate, in: Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change, Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, 1513–1766, https://doi.org/10.1017/9781009157896.013, 2021. a
Westra, S., Alexander, L. V., and Zwiers, F. W.: Global increasing trends in annual maximum daily precipitation, J. Climate, 26, 3904–3918, 2013. a
WMO: Guide to meteorological instruments and methods of observation, ISBN 978-92-63-100085, https://www.weather.gov/media/epz/mesonet/CWOP-WMO8.pdf (last access: 20 March 2024), 1996. a
Xu, P., Wang, D., Singh, V. P., Wang, Y., Wu, J., Lu, H., Wang, L., Liu, J., and Zhang, J.: Time-varying copula and design life level-based nonstationary risk analysis of extreme rainfall events, Hydrol. Earth Syst. Sci. Discuss. [preprint], https://doi.org/10.5194/hess-2019-358, 2019. a
Yiou, P., Ribereau, P., Naveau, P., Nogaj, M., and Brázdil, R.: Statistical analysis of floods in Bohemia (Czech Republic) since 1825, Hydrolog. Sci. J., 51, 930–945, 2006. a
Zeder, J. and Fischer, E. M.: Observed extreme precipitation trends and scaling in Central Europe, Weather and Climate Extremes, 29, 100266, https://doi.org/10.1016/j.wace.2020.100266, 2020. a, b, c
Zeppel, M. J. B., Wilks, J. V., and Lewis, J. D.: Impacts of extreme precipitation and seasonal changes in precipitation on plants, Biogeosciences, 11, 3083–3093, https://doi.org/10.5194/bg-11-3083-2014, 2014. a
Zolina, O., Simmer, C., Kapala, A., Bachner, S., Gulev, S., and Maechel, H.: Seasonally dependent changes of precipitation extremes over Germany since 1950 from a very dense observational network, J. Geophys. Res.-Atmos., 113, D06110, https://doi.org/10.1029/2007JD008393, 2008. a, b, c, d
Short summary
The paper introduces a statistical modeling approach describing daily extreme precipitation in Germany more accurately by including changes within the year and between the years simultaneously. The changing seasonality over years is regionally divergent and mainly weak. However, some regions stand out with a more pronounced linear rise of summer intensities, indicating a possible climate change signal. Improved modeling of extreme precipitation is beneficial for risk assessment and adaptation.
The paper introduces a statistical modeling approach describing daily extreme precipitation in...
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