Electrochemical carbon dioxide reduction (eCO2R) has emerged as a promising approach to produce high-value fuels and chemicals using renewable energy sources. In particular, Cu catalysts can convert CO2 to a wide range of hydrocarbon and oxygenate products, including CO, formate, ethanol and ethylene. However, the activity and selectivity of eCO2R is highly dependent on mass transport and the corresponding reaction microenvironment present under operating conditions. In this talk, we present an integrated framework that combines experiments with computational fluid dynamics (CFD) and density functional theory (DFT) based microkinetic modeling to predict the effect of varying mass transport on eCO2R in a custom-designed flow cell. The flow cell is designed with multiple electrolyte jets impinging onto the electrocatalyst surface for rapid and homogeneous reactant transport. We first demonstrate the effect of mass transport in a flow cell architecture by controlling the flow rate of CO2-saturated 0.1 M KHCO3 to a Cu electrocatalyst. Significant increases in the production rates of formate and CO are observed with increasing flow rate, as well as an enhancement in multi-carbon products at high overpotentials. Increasing flow rate promotes hydrogen evolution at low overpotentials but has little effect at large overpotentials, suggesting a transition from HCO3 - to water as the dominant proton donor with increasing overpotential. CFD simulations are employed to model the distribution of boundary layer thicknesses within the flow cell, which subsequently set the boundary conditions for the coupled 1D transport-microkinetic model. The calculated boundary layer thickness values are corroborated by experimentally measured transport-limited currents. Our model successfully predicts experimentally observed trends in selectivity with mass transport, such as an increase in the production of ethylene and ethanol at high flow rates due to enhanced CO2 transport. The slight suppression of hydrogen reduction at higher flow rates due to an increase in *CO coverage is also predicted and supported by experimental results. This work demonstrates the importance of incorporating transport processes in the CFD-DFT coupled modeling of device performance for electrochemical CO2 reduction.This work was performed under the auspices of the U.S. Department of Energy by Lawrence Livermore National Laboratory under Contract DE-AC52-07NA27344. LLNL release number: LLNL-ABS-857566.