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Abstract. High performance computing (HPC) has long been used in the disciplines of atmospheric and oceanic sciences, and remains the main tool of choice to extract numerical solutions to complex geophysical problems on the global scale, often accompanied with very large numbers of degrees of freedoms. However, with the growing recognition that the spatially distributed feedback from the land surface is important to weather and the climate system, representation of the land surface is established with increasingly complex (and physically complete) models, which often leads to the coupling of heterogeneous models such as numerical weather prediction (NWP) models and hydrological models. As a result, the spatial grids and the temporal resolutions have become finer and thereby computers with far greater computational and storage capacity are in great demand than those used in the past. Additionally, impact-focused studies that require coupling of accurate simulations of weather/climate systems as well as impact-measuring hydrological models that demand larger computer resources in its own right. In this paper, we present a preliminary analysis of an HPC-based hydrological modelling approach, which is aimed at utilising and maximising HPC power resource, to support the study on extreme weather impact due to climate change. Here, two case studies are presented through implementation on the HPC Wales platform of the UK mesoscale meteorological Unified Model (UM) UKV, alongside a Linux-based hydrological model, HYdrological Predictions for the Environment (HYPE). The results of this study suggest that high resolution rainfall estimation produced by the UKV has similar performance to that of NIMROD radar rainfall products as input in a hydrological model, but with the added-value of much extended forecast lead-time.

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