Floods are the most frequent natural disaster and due to climate change the frequency and intensity of these events are increasing. Therefore, it is becoming increasingly important to obtain accurate estimations of extreme discharges. Statistical modelling is widely used to estimate extreme discharges by fitting observed extreme discharges to an extreme value distribution. However, limited historical data makes it difficult to confidently model the tail behavior of extremes. Additionally, several modelling assumptions impact extreme discharge estimates including selection of the nonstationary method, extreme value distribution, parameter estimation method, and the impact of seasonality. In an effort to reduce uncertainties, a new method has been developed to derive design discharges for the Meuse in the Netherlands. This method, GRADE (Generator of Rainfall and Discharge Extremes) consists of three components: a stochastic weather generator, a hydrological model, and an extreme value analysis (EVA). However, the stochastic weather generator is not capable of producing daily rainfall that exceeds the range of historical data. Therefore, a physically based climate model, RACMO, is now being studied. RACMO is capable of generating 1,040 years of synthetic meteorological data that can be routed in a hydrological model to obtain 1,040 years of synthetic discharges. The physically based climate model makes it possible to capture the underlying physical processes of extreme events and the hydrological model can provide discharge information at locations where there are no observations. This thesis evaluates the impact various modelling assumptions have on estimated discharges using synthetic data generated by the RACMO through application of a case study in the Meuse.