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Reliable flood estimates are needed for designing safe and cost-effective flood protection structures. Classical flood estimation methods applied for deriving such estimates focus on peak discharge and neglect other important flood characteristics such as flood volume and the interdependence among different flood characteristics. Furthermore, they do not account for potential nonstationarities in hydrological time series due to climate change. The consideration of both the interdependence between peak discharge and flood volume and its nonstationarity might help us derive more reliable flood estimates. A few studies have looked at changes in the general dependence of peak discharge and flood volume for small sets of catchments and explored ways of modeling such changes. However, spatial analyses of trends in this dependence or in their climatological drivers have not been carried out. The aim of this study was to help close this knowledge gap by first quantifying trends in the general dependence between peak discharge and flood volume as described by Kendall's tau on a spatially comprehensive data set of 307 catchments in Switzerland. Second, potential climatological drivers for changes in the dependence between peak discharge and flood volume were identified. Our results show that the dependence between peak discharge and flood volume and its trends are spatially heterogeneous. This pattern cannot be explained by one driver only but by an interplay of changes in precipitation, snowmelt, and soil moisture. Both the trends and the links between drivers and trends depend on the climate model chain considered and are therefore uncertain.