Motivated by the question of whether and how a state-of-the-art regional chemical transport model (CTM) can facilitate characterization of CO<sub>2</sub> spatiotemporal variability and verify CO<sub>2</sub> fossil-fuel emissions, we for the first time applied the Community Multiscale Air Quality (CMAQ) model to simulate CO<sub>2</sub>. This paper presents methods, input data, and initial results for CO<sub>2</sub> simulation using CMAQ over the contiguous United States in October 2007. Modeling experiments have been performed to understand the roles of fossil-fuel emissions, biosphere–atmosphere exchange, and meteorology in regulating the spatial distribution of CO<sub>2</sub> near the surface over the contiguous United States. Three sets of net ecosystem exchange (NEE) fluxes were used as input to assess the impact of uncertainty of NEE on CO<sub>2</sub> concentrations simulated by CMAQ. Observational data from six tall tower sites across the country were used to evaluate model performance. In particular, at the Boulder Atmospheric Observatory (BAO), a tall tower site that receives urban emissions from Denver, CO, the CMAQ model using hourly varying, high-resolution CO<sub>2</sub> fossil-fuel emissions from the Vulcan inventory and CarbonTracker optimized NEE reproduced the observed diurnal profile of CO<sub>2</sub> reasonably well but with a low bias in the early morning. The spatial distribution of CO<sub>2</sub> was found to correlate with NOx, SO<sub>2</sub>, and CO, because of their similar fossil-fuel emission sources and common transport processes. These initial results from CMAQ demonstrate the potential of using a regional CTM to help interpret CO<sub>2</sub> observations and understand CO<sub>2</sub> variability in space and time. The ability to simulate a full suite of air pollutants in CMAQ will also facilitate investigations of their use as tracers for CO<sub>2</sub> source attribution. This work serves as a proof of concept and the foundation for more comprehensive examinations of CO<sub>2</sub> spatiotemporal variability and various uncertainties in the future. Implications: Atmospheric CO<sub>2</sub> has long been modeled and studied on continental to global scales to understand the global carbon cycle. This work demonstrates the potential of modeling and studying CO<sub>2</sub> variability at fine spatiotemporal scales with CMAQ, which has been applied extensively, to study traditionally regulated air pollutants. The abundant observational records of these air pollutants and successful experience in studying and reducing their emissions may be useful for verifying CO<sub>2</sub> emissions. Although there remains much more to further investigate, this work opens up a discussion on whether and how to study CO<sub>2</sub> as an air pollutant.