In this study, Earth Observation (EO) data describing urban morphology and thermal properties are used to configure the urban representation of Paris in the Weather Research and Forecasting (WRF) model. Model performance is assessed in convective permitting simulations (3 km) in three configurations covering summer 2020: (i) a baseline simulation employing a bulk parameterization of urban areas (i.e. BULK); (ii) a simulation using the BEP–BEM urban canopy model with default urban parameter values (CTRL); and (iii) a simulation in which the BEP–BEM urban canopy parameters are specifically adapted to Paris using EO–derived information (PAR). Comparison with observations from the Météo–France RADOME meteorological network indicates that the BULK simulation produces a systematic warm bias, with a persistent overestimation of nighttime temperatures. Coupling of the urban canopy model leads to an improved overall model performance relative to the BULK configuration for summer–mean conditions. Both BEP–BEM simulations are comparable, with the best performance during summer daytime achieved for the CTRL (0.93 °C lower bias compared to BULK) and during summer nighttime for the PAR simulation (0.22 °C lower bias compared to BULK) over urban grid cells. Over non–urban grid cells, the best performance is exhibited for the PAR simulation with a summer daytime bias of -0.26 °C and a summer nighttime bias of 1.62 °C. The simulated urban–rural temperature contrast for both BEP–BEM simulations is improved compared to BULK, resulting in a more realistic representation of the Urban Heat Island (UHI). Applying the Local Climate Zones (LCZ) classification, which accounts for intraurban differences in urban form and land cover, an analysis was conducted for each urban–type LCZ class present within the Paris urban area, linking each urban station to its nearest LCZ grid cell, enabling station–based comparisons by urban–type LCZ category. This analysis indicates that the PAR configuration better captures spatial urban temperature variability, reflecting the differences in urban forcing introduced by the EO data. During the heatwave of August 2020, the model – regardless of the configuration – becomes warmer and particularly over the urbanized LCZs. The PAR simulation exhibits pronounced temperature overestimations in the city center, due to the methodology adopted for EO data implementation and the selection of the area from which the EO data were derived, leading to urban characteristics which intensified heat storage and trapping. Non–urban areas are better simulated in both BEP–BEM simulations compared to the BULK. Our results demonstrate the added value of a) the coupling of an urban canopy model to WRF and b) the city–tailored configuration of urban canopy morphological parameters in convection–permitting regional climate simulations. The PAR experiment further illustrates the potential of EO–derived datasets to inform urban canopy parameter configurations, enabling a more detailed representation of the urban form and improved simulation of UHI characteristics.