AbstractClimate change is expected to increase heavy rainfall with concomitant increases in flooding1. Causes of increased heavy rainfall include higher water holding capacity of a warmer atmosphere and changes in the atmospheric circulation patterns2, which may translate into future heavy rainfall increases in most of Europe3. However, gathering of evidence on the time evolution of past changes has been hampered by data limitations and measurement uncertainties, in particular for short rainfall durations such as one hour. Here we show an 8% increase in daily and a 15% increase in hourly heavy rainfall in the last four decades by analysing a new data set comprising 883 stations in Central Europe from 1900 to 2020. The increase is fully consistent between two independent networks and occurs after a short retarding phase. Hourly rainfall changes are aligned with temperature increases with a sensitivity of 7% increase per 1°C of warming in line with Clausius-Clapeyron scaling. Daily rainfall changes, however, are aligned with atmospheric circulation indices with little correlation to air temperature, which suggests a bigger role of atmospheric circulation modes than previously thought. Daily heavy rainfall changes are remarkably consistent with the observed flood increases of about 8% in large catchments. Hourly heavy rainfall changes are similarly consistent with flood changes in small catchments, but the flood increase is stronger (25% in the last four decades). Climate adaptation measures in flood management may therefore be more pressing for rivers draining smaller catchment areas than for large rivers. External DatasetsMonthly air temperature data (HISTALP)File:data/external/HISTALP_AT_R01_T01.txtSourcehttp://www.zamg.ac.at/histalp/dataset/station/csv.phpReferenceAuer, I., Böhm, R., Jurkovic, A., Lipa, W., Orlik, A., Potzmann, R., Schöner, W., Ungersböck, M., Matulla, C., Briffa, K., Jones, P., Efthymiadis, D., Brunetti, M., Nanni, T., Maugeri, M., Mercalli, L., Mestre, O., Moisselin, J.-M., Begert, M., Müller-Westermeier, G., Kveton, V., Bochnicek, O., Stastny, P., Lapin, M., Szalai, S., Szentimrey, T., Cegnar, T., Dolinar, M., Gajic-Capka, M., Zaninovic, K., Majstorovic, Z. and Nieplova, E. (2007), HISTALP—historical instrumental climatological surface time series of the Greater Alpine Region. Int. J. Climatol., 27: 17-46. https://doi.org/10.1002/joc.1377 Meridionality and persistency indexFile:data/external/df_MFI.rdaReference:Haslinger, K., Hofstätter, M., Schöner, W. et al. Changing summer precipitation variability in the Alpine region: on the role of scale dependent atmospheric drivers. Clim Dyn 57, 1009–1021 (2021). https://doi.org/10.1007/s00382-021-05753-5 Cyclone tracking dataFile:data/external/tracking_data.rdsReference:Wernli, H. & Schwierz, C. Surface Cyclones in the ERA-40 Dataset (1958–2001). Part I: Novel Identification Method and Global Climatology. J. Atmospheric Sci. 63, 2486–2507 (2006).Hofstätter, M., Chimani, B., Lexer, A. & Blöschl, G. A new classification scheme of European cyclone tracks with relevance to precipitation. Water Resour. Res. 52, 7086–7104 (2016) Sulphate aerosol data (reconstruction from ice cores from the Colle Gnifetti ice cap)File:data/external/df_aer_so4.RDataSource/doi:https://doi.org/10.1594/PANGAEA.894787Reference:Sigl, M et al. (2018): 19th century glacier retreat in the Alps preceded the emergence of industrial black carbon deposition on high-alpine glaciers. The Cryosphere, 12(10), 3311-3331, https://doi.org/10.5194/tc-12-3311-2018 Sulphate aerosol data (measurements from snow pack at Sonnblick observatory)File:data/external/SO4_Sonnblick.txtReferenceMarion Greilinger, Wolfgang Schöner, Wilfried Winiwarter, Anne Kasper-Giebl, (2016) Temporal changes of inorganic ion deposition in the seasonal snow cover for the Austrian Alps (1983–2014), Atmospheric Environment, Volume 132, 141-152, https://doi.org/10.1016/j.atmosenv.2016.02.040. Country borders, digital elevation modelFiles:data/external/dem_gar.RDatadata/external/europe.RDataSource:https://www.eea.europa.eu/en/datahub/datahubitem-view/94438969-2dd5-4ba3-b708-e4d29a8b7699https://www.eea.europa.eu/en/datahub/datahubitem-view/d08852bc-7b5f-4835-a776-08362e2fbf4b WorkflowScript                              --> Result to feed into plotting scripts                    ----------------------------------------------------------------------xprec_daily.r                    --> data/processed/dfplot_dr999.rds                                      --> data/processed/dfplot_dr99.rds                                      --> data/processed/dfplot_dr99_witherror.rds                                      --> data/processed/dfplot_dr9.rds                        xprec_hourly.r                 --> data/processed/dfplot_hr99.rds                                      --> data/processed/dfplot_hr99_witherror.rds                                      --> data/processed/dfplot_hr9.rds                temperature_scaling.r      --> data/processed/df_gsa_hr_t2m.rds                                       --> data/processed/df_hyd_hr_t2m.rds                                       --> data/processed/df_hr_clim_scaling.rds                                       --> data/processed/df_hr_inst_scaling_plot.rds                                       --> data/processed/df_hr_inst_scaling_qr.rds                                       --> data/processed/df_dr_clim_scaling.rds                                       --> data/processed/df_dr_inst_scaling_plot.rds                                       --> data/processed/df_dr_inst_scaling_qr.rds

AbstractClimate change is expected to increase heavy rainfall with concomitant increases in flooding1. Causes of increased heavy rainfall include higher water holding capacity of a warmer atmosphere and changes in the atmospheric circulation patterns2, which may translate into future heavy rainfall increases in most of Europe3. However, gathering of evidence on the time evolution of past changes has been hampered by data limitations and measurement uncertainties, in particular for short rainfall durations such as one hour. Here we show an 8% increase in daily and a 15% increase in hourly heavy rainfall in the last four decades by analysing a new data set comprising 883 stations in Central Europe from 1900 to 2020. The increase is fully consistent between two independent networks and occurs after a short retarding phase. Hourly rainfall changes are aligned with temperature increases with a sensitivity of 7% increase per 1°C of warming in line with Clausius-Clapeyron scaling. Daily rainfall changes, however, are aligned with atmospheric circulation indices with little correlation to air temperature, which suggests a bigger role of atmospheric circulation modes than previously thought. Daily heavy rainfall changes are remarkably consistent with the observed flood increases of about 8% in large catchments. Hourly heavy rainfall changes are similarly consistent with flood changes in small catchments, but the flood increase is stronger (25% in the last four decades). Climate adaptation measures in flood management may therefore be more pressing for rivers draining smaller catchment areas than for large rivers. External DatasetsMonthly air temperature data (HISTALP)File:data/external/HISTALP_AT_R01_T01.txtSourcehttp://www.zamg.ac.at/histalp/dataset/station/csv.phpReferenceAuer, I., Böhm, R., Jurkovic, A., Lipa, W., Orlik, A., Potzmann, R., Schöner, W., Ungersböck, M., Matulla, C., Briffa, K., Jones, P., Efthymiadis, D., Brunetti, M., Nanni, T., Maugeri, M., Mercalli, L., Mestre, O., Moisselin, J.-M., Begert, M., Müller-Westermeier, G., Kveton, V., Bochnicek, O., Stastny, P., Lapin, M., Szalai, S., Szentimrey, T., Cegnar, T., Dolinar, M., Gajic-Capka, M., Zaninovic, K., Majstorovic, Z. and Nieplova, E. (2007), HISTALP—historical instrumental climatological surface time series of the Greater Alpine Region. Int. J. Climatol., 27: 17-46. https://doi.org/10.1002/joc.1377 Meridionality and persistency indexFile:data/external/df_MFI.rdaReference:Haslinger, K., Hofstätter, M., Schöner, W. et al. Changing summer precipitation variability in the Alpine region: on the role of scale dependent atmospheric drivers. Clim Dyn 57, 1009–1021 (2021). https://doi.org/10.1007/s00382-021-05753-5 Cyclone tracking dataFile:data/external/tracking_data.rdsReference:Wernli, H. & Schwierz, C. Surface Cyclones in the ERA-40 Dataset (1958–2001). Part I: Novel Identification Method and Global Climatology. J. Atmospheric Sci. 63, 2486–2507 (2006).Hofstätter, M., Chimani, B., Lexer, A. & Blöschl, G. A new classification scheme of European cyclone tracks with relevance to precipitation. Water Resour. Res. 52, 7086–7104 (2016) Sulphate aerosol data (reconstruction from ice cores from the Colle Gnifetti ice cap)File:data/external/df_aer_so4.RDataSource/doi:https://doi.org/10.1594/PANGAEA.894787Reference:Sigl, M et al. (2018): 19th century glacier retreat in the Alps preceded the emergence of industrial black carbon deposition on high-alpine glaciers. The Cryosphere, 12(10), 3311-3331, https://doi.org/10.5194/tc-12-3311-2018 Sulphate aerosol data (measurements from snow pack at Sonnblick observatory)File:data/external/SO4_Sonnblick.txtReferenceMarion Greilinger, Wolfgang Schöner, Wilfried Winiwarter, Anne Kasper-Giebl, (2016) Temporal changes of inorganic ion deposition in the seasonal snow cover for the Austrian Alps (1983–2014), Atmospheric Environment, Volume 132, 141-152, https://doi.org/10.1016/j.atmosenv.2016.02.040. Country borders, digital elevation modelFiles:data/external/dem_gar.RDatadata/external/europe.RDataSource:https://www.eea.europa.eu/en/datahub/datahubitem-view/94438969-2dd5-4ba3-b708-e4d29a8b7699https://www.eea.europa.eu/en/datahub/datahubitem-view/d08852bc-7b5f-4835-a776-08362e2fbf4b WorkflowScript                              --> Result to feed into plotting scripts                    ----------------------------------------------------------------------xprec_daily.r                    --> data/processed/dfplot_dr999.rds                                      --> data/processed/dfplot_dr99.rds                                      --> data/processed/dfplot_dr99_witherror.rds                                      --> data/processed/dfplot_dr9.rds                        xprec_hourly.r                 --> data/processed/dfplot_hr99.rds                                      --> data/processed/dfplot_hr99_witherror.rds                                      --> data/processed/dfplot_hr9.rds                temperature_scaling.r      --> data/processed/df_gsa_hr_t2m.rds                                       --> data/processed/df_hyd_hr_t2m.rds                                       --> data/processed/df_hr_clim_scaling.rds                                       --> data/processed/df_hr_inst_scaling_plot.rds                                       --> data/processed/df_hr_inst_scaling_qr.rds                                       --> data/processed/df_dr_clim_scaling.rds                                       --> data/processed/df_dr_inst_scaling_plot.rds                                       --> data/processed/df_dr_inst_scaling_qr.rds