We present a synthesis Bayesian inverse method to optimize one year of daily fluxes at model resolution (50x50 km over Europe) by inversion of continuous CO₂ measurements, daily averaged over Europe (10 sites). Based on a synthetic data analysis, we studied the impact of three different spatial and temporal correlations on flux errors. We found that the present network is too sparse to efficiently constrain European fluxes at model resolution even with the assumption of perfect transport. However, the agreement between the optimized fluxes and the true fluxes is improved when aggregated in space and time, mainly for 8-10 days fluxes over Western Europe. This region is indeed surrounded by our network. The spatial correlation scheme used was found to have a negligible impact on this agreement. Adding a white noise on pseudo-data to simulate transport model errors largely degrades the agreement. Using real data, European flux variations becomes unreasonably large due to the inability of our transport model to properly represent the CO₂ concentrations at continental sites.