Understanding the variability and sources of atmospheric CO<sub>2</sub> is essential for improving greenhouse gas monitoring and model performance. This study investigates temporal CO<sub>2</sub> variability at the Xianghe site in China, which hosts both remote sensed (TCCON-affiliated) and in situ (PICARRO) observations. Using the Weather Research and Forecast model coupled with Chemistry, in its greenhouse gas option (WRF-GHG), we performed a one-year simulation of surface and column-averaged CO<sub>2</sub> mole fractions, evaluated model performance and conducted sensitivity experiments to assess the influence of key model configuration choices. The model captured the temporal variability of column-averaged mole fraction of CO<sub>2</sub> (XCO<sub>2</sub>) reasonably well (r=0.7), although a persistent bias in background values was found. A July 2019 heatwave case study further demonstrated the model’s ability to reproduce a synoptically driven anomaly. Near the surface, performance was good during afternoon hours (r=0.75, MBE=–1.65 ppm), nighttime mole fractions were overestimated (MBE = 6.51 ppm), resulting in an exaggerated diurnal amplitude. Sensitivity tests revealed that land cover data, vertical emission profiles, and adjusted VPRM-parameters (Vegetation Photosynthesis and Respiration Model) can significantly influence modeled mole fractions, particularly at night. Tracer analysis identified industry and energy as dominant sources, while biospheric fluxes introduced seasonal variability – acting as a moderate sink in summer for XCO<sub>2</sub> and a net source in most months near the surface. These findings demonstrate the utility of WRF-GHG for interpreting temporal patterns and sectoral contributions to CO<sub>2</sub> variability at Xianghe, while emphasizing the importance of careful model configuration to ensure reliable simulations.