In this study a modelling system consisting of Mesoscale Model (MM5), Sparse Matrix Operator Kernel Emissions (SMOKE) and Community Multiscale Air Quality (CMAQ) model has been applied to a summer photochemical period in southeast England, UK. Ozone (O3), nitrogen dioxide (NO2) and particulate matter (PM2.5) concentrations modelled with different horizontal grid resolutions (9 and 3 km) were evaluated against available ground-level observations from the UK Automatic Urban and Rural Network (AURN) and London Air Quality Network (LAQN) for the period of 24–28 June 2001 with a focus on O3 predictions. This effort, which represents the first comprehensive performance evaluation of the modelling system over a UK domain, reveals that CMAQ's ability to reproduce surface O3 observations varies with O3 concentrations. It underpredicts O3 mixing ratios on high-O3 days and overpredicts the maximum and minimum hourly O3 values for most low-O3 days. Model sensitivity analysis with doubled anthropogenic NOx or volatile organic compounds (VOC) emissions and analysis of the daylight-averaged levels of OX (sum of O3 and NO2) as a function of NOx revealed that the undereprediction of peak O3 concentrations on high-O3 days is caused by the underprediction of regional contribution and to a lesser extent local production, which might be related to the underestimation of European emissions in EMEP inventory and the lacked reactivity of the modelled atmosphere. CMAQ systematically underpredicts hourly NO2 mixing ratios but captures the temporal variations. The normalized mean bias for hourly NO2, although much larger than that for O3, falls well within the generally accepted range of −20% to −50%. CMAQ with both resolutions (9 and 3 km) significantly underpredicts PM2.5 mass concentrations and fails to reproduce its temporal variations. While model performance for O3 and PM2.5 are not very sensitive to model grid resolutions, a better agreement between modelled and measured hourly NO2 mixing ratios was achieved with higher resolution. Further investigation into the uncertainties in meteorological input, uncertainties in emissions, as well as representation of physical and chemical processes (e.g. chemical mechanism) in the model is needed to identify the causes for the discrepancies between observations and predictions.