Historical and future changes of surface ozone over China from CMIP6 models, including an assessment of present-day uncertainties in model prediction

Abstract

Ozone (O3) contributes to global climate change and poses a direct threat to human health. This study investigates the historical variability, future projections, and associated present-day uncertainties of surface O3 concentrations over China using simulations from nine CMIP6 models and observational data from the Tracking Air Pollution in China (TAP) dataset. A multi-model ensemble mean (MME), constructed using an equal-weighted approach, is used to evaluate model uncertainties across different seasons, underlying surface types, total cloud cover, and PM2.5 concentrations, and assess model performance under future Shared Socioeconomic Pathway (SSP) scenarios. The results show that the MME captures the pronounced seasonal cycle of surface O3, with higher concentrations during June–August (JJA, ∼ 105 µg m−3) and lower values during December–February (DJF, ∼ 55 µg m−3), but underestimates O3 across most regions of China, particularly in East China. Model performance varies with environmental conditions, showing lower bias, MAE, and RMSE over natural land surfaces than over anthropogenic surfaces. The O3 bias is minimized under cloudy conditions, maximized under partly cloudy conditions, and generally increases with rising PM2.5 concentrations before declining beyond a certain threshold. Over the historical period, the MME simulates a substantial increase in annual mean surface O3 across China (∼ 39.3 µg m−3). Future projections indicate continued O3 increases under weak mitigation (SSP3-7.0), with East China rising by 26.9 %, and widespread decreases under strong mitigation (SSP1-2.6), particularly in Southwest and South China (> 30 µg m−3). Analysis of model spread and its drivers indicates that uncertainties in surface O3 projections arise from the combined effects of emissions (including precursors and PM2.5), climate conditions, and model representations of chemistry and circulation. Improving the understanding of these coupled influences is essential for enhancing the reliability of regional O3 projections and for informing effective air quality and climate mitigation strategies in China.