Quantifying urban CO<sub>2</sub> emissions using spaceborne total column observations requires atmospheric transport models capable of resolving fine-scale spatial and temporal variability across metropolitan areas. Using the <em>Grand Paris</em> region as a testbed, we evaluated the sensitivity of simulated CO<sub>2</sub> fields to spatial resolution and model physics within the Weather Research and Forecasting model coupled to a high-resolution fossil fuel emissions inventory. Simulations were conducted at mesoscale (900 m) and Large-Eddy Simulation (LES) resolutions (300 m and 100 m), and evaluated against dense urban CO<sub>2</sub> observations from the Paris Picarro and rooftop mid-cost sensors network. Horizontal resolution strongly affects plume morphology: high-resolution simulations produce sharper gradients, higher peak mixing ratios and more realistic temporal variability. LES simulations capture localized enhancements and intra-urban variability more accurately than the 900 m mesoscale run, which systematically underestimates spatial contrasts by a factor of 2–3. Aggregation experiments indicate that averaging LES simulations to 900 m significantly modifies plume magnitudes and spatial gradients. Domain-mean relative errors for near-surface CO<sub>2</sub> reach 29–34 % in winter and 59–61 % in summer, while column-integrated XCO<sub>2</sub> shows 21–22 % errors in winter and 58–59 % in summer, with maxima exceeding 70 %. While LES improves the representation of urban CO<sub>2</sub> variability, it increases sensitivity to local wind and inventory uncertainties, meaning misplaced point sources can cause large local biases. These findings emphasize the need for inversion frameworks able to incorporate scale-dependent transport and inventory uncertainties to ensure unbiased top-down emission estimates and accurate spatial attribution within city domains.