Seasonal Prediction of China's Overall Ozone Pollution

Ozone pollution is gradually emerging as the biggest obstacle to improving air quality in China, causing severe harm to human health and the natural environment. However, the future trend of China's overall ozone pollution remains poorly understood and the impact of global sea surface temperature anomalies (SSTA) on the long-term predictability of surface ozone in China remains a challenge. In this study, we use eigenanalysis emerges to effectively characterize dominant surface ozone pattern in China and establish cross-correlations between it and global SSTA time series. In summer, the daily maximum 8-hour average (MDA8) ozone anomaly in June-July-August (JJA) is mainly regulated by the West Pacific Subtropical High and the North Pacific High. In winter, the climate patterns related to the MDA8 ozone anomaly in December-January-February (DJF) are affected by the North Atlantic Oscillation ， the Madden-Julian Oscillation and others. We propose a multivariate regression model capable of predicting surface ozone patterns at least three months in advance. Our model's evaluation, using a testing dataset, shows an R-squared value of around 0.5 between predicted and observed data, surpassing the statistical significance threshold. This suggests the feasibility and potential applicability of our predictive model for forecasting and mitigating surface ozone in China.