We present an inverse modeling framework designed to constrain CO2 budgets at regional scales. The approach captures atmospheric transport processes in high spatiotemporal resolution by coupling a mesoscale model with Lagrangian Stochastic backward trajectories. Terrestrial biosphere CO2 emissions are generated through a simple diagnostic flux model that splits the net ecosystem exchange into its major components of gross primary productivity and autotrophic and heterotrophic respirations. The modeling framework assimilates state-of-the-art data sets for advected background CO2 and anthropogenic fossil fuel emissions as well as highly resolved remote sensing products. We introduce a Bayesian inversion setup, optimizing a posteriori flux base rates for surface types that are defined through remote sensing information. This strategy significantly reduces the number of parameters to be optimized compared with solving fluxes for each individual grid cell, thus permitting description of the surface in a very high resolution. The model is tested using CO2 concentrations measured in the fall and winter of 2006 at two AmeriFlux sites in Oregon. Because this database does not cover a full seasonal cycle, we focus on conducting model sensitivity tests rather than producing quantitative CO2 flux estimates. Sensitivity tests on the influence of spatial and temporal resolution indicate that optimum results can be obtained using 4 h time steps and grid sizes of 6 km or less. Further tests demonstrate the importance of dividing biome types by ecoregions to capture their different biogeochemical responses to external forcings across climatic gradients. Detailed stand age information was shown to have a positive effect on model performance.