Urban environments face increasing challenges related to thermal discomfort and heat stress due to climate change and rapid urbanization. This study presents a methodology for developing a 4D digital thermal comfort model by integrating time-series pedestrian-level microclimate measurements with a 3D urban model. A mobile field campaign in Rabat, Morocco, collected air temperature, relative humidity, wind speed, and black globe temperature at 22 sampling points over 9 summer days. These measurements were used to calculate the Universal Thermal Climate Index (UTCI). A detailed 3D urban model was created from laser scanning data, featuring Level of Detail 3 (LoD3) for buildings and trees of interest. UTCI point measurements were spatially interpolated using Empirical Bayesian Kriging 3D combined with shading analysis. The interpolated hourly 3D UTCI maps were combined into a voxel dataset through an automated Python workflow, enabling dynamic visualization of thermal comfort evolution. Results revealed distinct diurnal patterns with UTCI values ranging from 17°C (no thermal stress) to 43°C (very strong heat stress), with relatively consistent spatial patterns. Vegetation demonstrated significant cooling effects, with broad-canopied species providing 3-5°C UTCI reduction compared to unshaded areas, while tall palm trees offered minimal cooling, as expected. Interestingly, some vegetation areas exhibited complex thermal behaviour, appearing slightly warmer during morning and evening periods. The approach provides insights into the temporal dynamics of pedestrian-level thermal comfort, establishing a promising foundation for climate-responsive urban planning interventions in Moroccan cities.