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The impact of ambient air pollution on human health, particularly fine particulate matter (PM2.5) and tropospheric ozone (O3), is a critical global concern. Atmospheric chemical transport models (CTMs) are widely used to predict air pollutant concentrations and assess associated health risks. However, there is a need to better understand how the horizontal resolution of these models influences their accuracy, especially in future assessments. In this study, we compared the performance of global low-resolution CTMs with high-resolution nested simulations for estimating O3 and PM2.5 concentrations. The models were validated against observational data to determine their accuracy across different spatial scales and to evaluate their suitability for future scenario assessments. Our findings demonstrate that while the nested-grid simulations improved the reproducibility of regional observations, especially in areas with complex topography or localized emissions, the overall global-scale performance of the model did not significantly benefit from higher resolution. Additionally, the differences in global health and agricultural impacts between low- and high-resolution simulations were minor and within the range of uncertainty typically associated with emission inventories and CTMs. However, for specific regional studies or policy applications, higher resolution may offer improved accuracy. Ultimately, the current low-spatial-resolution model provides sufficient accuracy for many global-scale applications, but the choice of resolution should be carefully considered depending on the specific objectives of the study especially in future scenario.