Climate-driven deterioration of future ozone pollution in Asia predicted by machine learning with multisource data

Abstract. Ozone (O₃) is a secondary pollutant in the atmosphere formed by photochemical reactions that endangers human health and ecosystems. O₃ has aggravated in Asia in recent decades and will vary in the future. In this study, to quantify the impacts of future climate change on O₃ pollution, near-surface O₃ concentrations over Asia in 2020–2100 are projected using a machine learning (ML) method along with multisource data. The ML model is trained with combined O₃ data from a global atmospheric chemical transport model and real-time observations. The ML model is then used to estimate future O₃ with meteorological fields from multi-model simulations under various climate scenarios. The near-surface O₃ concentrations are projected to increase by 5–20 % over South China, Southeast Asia, and South India and less than 10 % over North China and Gangetic Plains under the high forcing scenarios in the last decade of 21^st century, compared to the first decade of 2020–2100. The O₃ increases are primarily owing to the favorable meteorological conditions for O₃ photochemical formation in most Asian regions. We also find that the summertime O₃ pollution over eastern China will expand from North China to South China and extend into the cold season in a warmer future. Our results demonstrate the important role of climate change penalty on Asian O₃ in the future, which provides implications for environmental and climate strategies of adaptation and mitigation.