Hydrological change modelling methodologies generally use climate models outputs to force hydrological simulations under changed conditions. There are nested sources of uncertainty throughout this methodology, including choice of climate model and subsequent bias correction methods. This Ph.D. study evaluates the uncertainty of the impact of climate change in hydrological simulations given multiple climate models and bias correction methods of varying complexity. Three distribution based scaling methods (DBS) were developed and benchmarked against a more simplistic and commonly used delta change (DC) approach. These climate model projections were then used to force hydrological simulations under climate change for the island Sjælland in Denmark to analyse the contribution of different climate models and bias correction methods to overall uncertainty in the hydrological change modelling methodology for basin discharge and groundwater heads. The ensemble of 11 climate models varied in strength, significance, and sometimes in direction of the climate change signal. The more complex daily DBS correction methods were more accurate at transferring precipitation changes in mean as well as the variance, and improving the characterisation of day to day variation as well as heavy events. However, the most highly parameterised of the DBS methods were less robust under climate change conditions. The spatial characteristics of groundwater head and stream discharge were best represented by DBS methods applied at the grid scale. Flux and state hydrological outputs which integrate responses over time and space showed more sensitivity to precipitation mean spatial biases and less so on extremes. In the investigated catchments, the projected change of groundwater levels and basin discharge between current and future conditions was found to be modest across multiple climate models and bias correction methods. The choice of RCM contributes almost all of the uncertainty across precipitation inputs, explaining 99% of uncertainty in total annual precipitation, while choice of bias correction method contributes far less to overall uncertainty, contributing to 7% of variance in total annual precipitation values. Similarly choice of climate model contributes to almost all of the variance in stream discharge, 97% in total annual, while choice of bias correction method contributes to 10% of uncertainty in total annual discharge.