Floods are the most common and widely distributed natural hazard, threatening life and property worldwide. Governments worldwide are facing significant challenges associated with flood hazard, specifically: increasing urbanization; against the background of uncertainty associated with increasing climate variability under climate change. Thus, flood hazard assessments need to consider climate change uncertainties explicitly. This paper explores the role of climate change uncertainty through uncertainty analysis in flood modelling through a probabilistic framework using a Monte Carlo approach and is demonstrated for case study catchment. Different input, structure and parameter uncertainties were investigated to understand how important the role of a non-stationary climate may be on future extreme flood events. Results suggest that inflow uncertainties are the most influential in order to capture the range of uncertainty in inundation extent, more important than hydraulic model parameter uncertainty, and thus, the influence of non-stationarity of climate on inundation extent is critical to capture. Topographic controls are shown to create tipping points in the inundation-flow relationship, and these may be useful and important to quantify for future planning and policy. Full Monte Carlo analysis within the probabilistic framework is computationally expensive, and there is a need to explore more timeefficient strategies which may result in a similar estimate of the full uncertainty. Simple uncertainty quantification techniques such as Latin hypercube sampling approaches were tested to reduce computational burden.