Accurately estimating the surface mass balance (SMB) of the Greenland Ice Sheet (GrIS) is essential to quantify its contribution to sea-level rise. The polar regional atmospheric climate model MAR is widely used to simulate GrIS SMB and to force ice sheet dynamics models, highlighting the need for a thorough evaluation. Here, we evaluate the latest MAR version (MARv3.14) over Greenland at 5 km spatial resolution and examine the impact of coarser resolutions (10–30 km) on the simulated SMB and its components. MAR outputs are compared to a range of independent observations, including in situ SMB measurements, automatic weather station (AWS) records of near-surface meteorological variables, satellite-derived melt extent, and albedo products. At 5 km, MAR reproduces the observed SMB with a root-mean-square error (RMSE) of 0.51 m and a correlation of 0.93. For near-surface meteorological variables and surface energy budget fluxes, the model RMSE is smaller than the corresponding observed natural variability (i.e., standard deviation), indicating non-significant model error. Prescribing bare-ice albedo improves the model performance in the ablation zone, while biases remain in the accumulation zone, suggesting that further improvements are required in the snow albedo scheme. In addition, simulated melt timing is consistent with satellite-based melt extent products. Sensitivity experiments reveal that discrepancies between simulations at different spatial resolutions are mostly limited to the ice sheet margins where strong SMB and topographic gradients occur, notably in the southeast of Greenland where precipitation peaks. Differences in integrated SMB generally remain small and mostly non-significant, while individual components, i.e., precipitation and runoff, exhibit larger resolution-dependent variations. We find that a resolution of at least 10 km is required to accurately capture the GrIS climate and SMB. Reducing computation time about 10-fold relative to 5 km simulation, a 10 km grid makes a good compromise for long-term climate projections.