Urban microclimate models are widely used to analyse urban heat island effects and thermal conditions at fine spatial scales. However, microscale models such as ENVI-met require detailed meteorological forcing data, which are often unavailable or insufficient in many urban environments, particularly in smaller cities or regions with sparse meteorological observation networks. This study presents a multi-scale modelling approach that uses outputs from the open-source Weather Research and Forecasting model coupled with an Urban Canopy Model (WRF-UCM) to generate forcing data for ENVI-met simulations. ERA5 reanalysis data and nested WRF-UCM domains are used to simulate meteorological conditions over Košice (Slovakia), and selected atmospheric variables are extracted and converted into meteorological forcing data for microscale ENVI-met simulations. Results demonstrate that WRF-UCM-derived forcing data can reproduce realistic temporal variability in key atmospheric parameters. Validation against observations showed that ENVI-met simulations forced by WRF-UCM improved mean absolute errors by 8.4% for air temperature, 6.8% for relative humidity, and 25.1% for wind speed compared with WRF-UCM alone. The proposed approach enables the application of microscale urban climate models in data-scarce environments while preserving physically consistent initial and boundary conditions. It also improves the representation of regional atmospheric influences, supporting more robust and transferable urban climate analyses.