Seasonal predictability of the dominant surface ozone pattern over China linked to sea surface temperature

Mitigation surface ozone pollution becomes increasingly pivotal in improving China's air quality. However, the impact of global sea surface temperature anomalies (SSTA) on the long-term predictability of China's surface ozone remains challenging. In this study, we employ eigen techniques to effectively characterize dominant surface ozone patterns over China, and establish cross-correlations between the dominant patterns and global SSTA time series. Our findings reveal that China's summer ozone pollution is strongly associated with crucial SSTA clusters linked to atmospheric circulations, i.e., the West Pacific Subtropical High and the Pacific-North American teleconnection pattern. For winter, ozone pollution is attributed to SSTA clusters related to the Southern Oscillation, the Madden-Julian Oscillation and others. We propose a multivariate regression model capable of predicting surface ozone patterns with a lead time of at least 3 months. Evaluation of our model using a testing dataset yields an R-value of around 0.5 between predicted and observed data, surpassing statistical significance threshold. This suggests the viability and potential applicability of our predictive model in surface ozone forecasting and mitigation strategies in China.