The flow distribution within the automotive catalytic converter is an important controlling factor on the overall conversion efficiency. Capturing the flow features minimizing the computational cost is the first important step towards the solution of the complex full engineering problem. In this work we present a novel approach that combines physical and numerical multi-resolution techniques in order to correctly capture the flow features inside an automotive catalytic converter. While Adaptive Mesh Refinement techniques are optimized in order to minimize the computational effort in the divergent region, a novel subgrid model is developed to describe the flow inside the catalytic substrate placed between the convergent and divergent regions. The proposed Adaptive Mesh Refinement methods are tested for two test cases representative of the flow features found in the divergent region of a catalytic converter. The performance of the new subgrid model is validated against the non-uniformity index and the radial velocity profile data obtained by Benjamin et al. (2002). The effective coupling of AMR techniques and the subgrid model significantly reduces the error of the numerical predictions to 5–15% in conditions where the full simulation of the problem is out of current computational capabilities.