Abstract. Surface ozone, a major air pollutant with profound implications for human health, ecosystems, and climate, shows long-term trends shaped by both anthropogenic and climatic drivers. Here, we develop a machine learning-based approach – the Fixed Emission Approximation (FEA) – to disentangle the effects of meteorological variability and anthropogenic emissions on summertime ozone trends in China. We identify three distinct phases of ozone trends corresponding to clean air actions. Anthropogenic emissions drove a +23.2 ± 1.1 μg m⁻3 increase in summer maximum daily 8-hour average ozone during 2013–2017, followed by a −4.6 ± 1.5 μg m⁻3 decrease during 2018–2020. However, during 2021–2023, extreme meteorological anomalies – including heatwaves and extended monsoon rainfall – emerged as key drivers of ozone variability. Satellite-derived formaldehyde-to-nitrogen dioxide ratios reveal widespread urban volatile organic compounds-limited regimes, with a shift toward nitrogen oxides-limited sensitivity under influence of heatwaves. Finally, we assess ozone trends under sustained climate warming from 1970 to 2023 based on the FEA framework. The results indicate a significant climate-driven increase in ozone levels across China's urban agglomerations, underscoring the amplifying role of climate change in ozone pollution. Together, these findings highlight the dual influence of anthropogenic and climatic factors on ozone pollution and emphasize the need for integrated strategies that couple emission mitigation with climate adaptation to effectively manage ozone risks in a warming world.
Fang et al. (Thu,) studied this question.