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Anthropogenic climate change is arguably the greatest challenge of modern times posing significant risks to natural resources and the environment. Socio-economic change, severe droughts, and environmental concerns focus attention upon sustainability of water supplies and the ability of water utilities to meet competing demands worldwide. The 2012 Climate Change Risk Assessment identified water security as one of the most significant climate threats facing the UK. It is now recognised that household water demand management could offer a low regret adaptation measure (both financially and environmentally) given large uncertainties about future climate and non-climatic pressures. This thesis uses Anglian Water Services (AWS) Golden 100 dataset to explore the climate sensitivity of historic micro-component water-use. This work contributes to a larger integrated assessment of the South-East England water system under the EPSRC Adaptation and Resilience to a Changing Climate Coordination Network (ARCC CN). The Golden 100 is a metered record of 100 households daily water consumption by basin, bath, dishwasher, external, kitchen sink, shower, WC and washing machine use. The archive also includes socio-economic information for each household, dates of the year and daily time series of observed minimum temperature, maximum temperature, sunshine hours, soil moisture deficit, concurrent, and antecedent rainfall amounts. The methodology developed within this research provides a portable approach to error trapping, formatting and mining large, complex water sector datasets, for exploring the relative sensitivities of micro-component metered water-use to weather/non-weather variables. This research recognises both the importance of the choice to use a micro-component and the volume used. As such, logistic and linear generalised regression techniques are employed to explore the relative sensitivity of these two aspects of water-use to climatic and non-climatic variables. The 2009 UK Climate Projections (UKCP09) projections and climate analogues are then used to bound a climate sensitivity analysis of the most weather-sensitive micro-components using temperature and rainfall scenarios for the 2050s and 2080s. This research provides empirical evidence that the most weather sensitive micro-components are external and shower water-use. A key contribution of this research to existing knowledge is the non-linear response of likelihood and volume of external water-use to average air temperatures. There is an abrupt increase in the likelihood of external water-use on days above ~15??C. Climate sensitivity analysis further suggests that by the 2080s, under a hotter/drier climate, average unmetered households could be 8% more likely to use external-water and expend ~9 litres more per day during the summer. For the same parameters, high water users (defined here as the 90th percentile) could consume ~13 litres more external water per day. Importantly, this research has re-affirmed the relative importance of behavioural drivers of water-use as manifested by pronounced day of week and bank holiday signatures in both the likelihood and volume of use statistics. As such, this prompts future studies and water management efforts to consider the impact of behavioural drivers as well as climate. It must be recognised that the small sample size of the Golden 100 combined with the Hawthorn effect, self-selection and sample biases in factors such as socio-economic status, billing method and occupancy rate all limit the sample representativeness of the wider population. As such, any predictions based on the data must be treated as illustrative rather than definitive. Furthermore, the results are probably specific to the demographic and socio-economic groups comprising the sample. Nonetheless, this research sheds new light into water-use within the home thereby adding value to a dataset that was not originally collected with household-level, weather-related research in mind.