Climate change impact assessments require local-scale climate scenarios. The climate change projections from Global Climate Models (GCMs) are difficult to use at local scale due to their coarse spatial and temporal resolution. It is important to have climate change scenarios based on GCMs climate projections GCMs ensembles, e.g. CMIP6, downscaled to local scale to account for their inherent uncertainty, and to generate a sufficient large number of realisations to account for inter-annual climate variability and low frequency but high impact extreme climatic events. A dataset of future climate change scenarios was therefore generated at 26 representative sites across the UK based on the latest CMIP6 multi-model ensemble downscaled to local-scale by using a stochastic weather generator LARS-WG 7.0. The data set provides 1,000 years of daily weather at each selected site for a baseline (1985-2015), and very near- (2030) and near-future (2050) climate change scenarios, based on five GCMs and two emission scenarios (Shared Socioeconomic Pathways - SSPs viz. SSP2-4.5 and SSP5-8.5). A total of 15 GCMs from the CMIP6 ensemble were integrated in LARS-WG 7.0. LARS-WG downscales future climate projections from the GCMs and incorporates changes at local scale in the mean climate, climatic variability, and extreme events by modifying the statistical distributions of the weather variables at each site. Based on the performance of the GCMs over northern Europe and their climate sensitivity, a subset of five GCMs was selected, viz.; ACCESS-ESM1-5, CNRM-CM6-1, HadGEM3-GC31-LL, MPI-ESM1-2-LR and MRI-ESM2-0. The selected GCMs are evenly distributed among the full set of 15 GCMs. The use of a subset of GCMs substantially reduces computational time, while allowing assessment of uncertainties in impact studies related to uncertain future climate projections arising from GCMs. The 1000 years of realisations of daily weather for the baseline as well as future climate change scenarios are helpful for estimating seasonality and inter-annual variation, and for detecting short, low frequency but high impact extreme climatic signals, such as heat waves, floods and drought events. The dataset can be used as an input to climate change impact models in various fields, including, land and water resources, agriculture and food production, ecology and epidemiology, and human health and welfare. Researchers, breeders, farm and programme managers, social and public sector leaders, and policymakers may benefit from this new dataset when undertaking impact assessment of climate change and decision support for mitigation and adaptation.