Abstract: PM_2.5 and O₃ were the main factors affecting urban and regional air quality in China. Exploring their pollution characteristics and predicting their concentrations are the basic work for the prevention and control of composite atmospheric pollution. The PM_2.5 and O₃ compound air pollution characteristics were analyzed using the concentration data of PM_2.5 and O₃ in Yichang and Wuhan from 2015 to 2023, which are two major cities in the middle reaches of the Yangtze River. The machine learning model explainable tool SHAP (shapely additive explanation) was used to reveal the contribution of meteorological factors such as temperature, humidity, precipitation, sunshine, wind speed, and other meteorological factors to the influence of PM_2.5 and O₃ concentrations. Based on nine deep learning methods including GRU (gated recurrent unit), a regression prediction model was constructed for PM_2.5 and O₃ concentrations, and their performance was evaluated. The results are as follows. (1) From 2015 to 2023, the O₃ concentrations in Yichang and Wuhan increased annually by 3.89 µg.m⁻³ and 2.73 µg.m⁻³ on average, with a more evident increase in summer and autumn. However, the PM_2.5 concentrations showed a significant decrease trend, with the trend rates of -3.59 µg.m⁻³.a⁻¹ and -3.36 µg.m⁻³.a⁻¹, respectively. The decrease was more notable in winter and spring, indicating the significant effectiveness of pollution control in recent years. (2) The monthly changes of PM_2.5 and O₃ concentrations showed “U” and “M” features, with a weak negative correlation between PM_2.5 and O₃. From 2015 to 2023, the compound air pollution days with high concentrations of both PM_2.5 and O₃ in Yichang and Wuhan were 60 and 39 days, respectively, which mainly concentrated in February to May and October to December. An annually decreasing trend was found, except for a slight increase in 2023. (3) Comparative analysis of a total of nine deep learning prediction models showed that four models, including GRU (gated recurrent unit), BIGRU (bidirectional gated recurrent units), Attention-GRU (attention gated recurrent unit), and Attention-BiGRU (attention bidirectional gated recurrent units), performed better in predicting PM_2.5 and O₃ concentrations in Yichang and Wuhan. Particularly, the GRU model had the shortest running time, which could effectively improve PM2.5 and O₃ concentration prediction performance and services. (4) Compared with GRU, the root mean square error for O₃ concentration prediction of the regression prediction model was reduced by 5% and 8% in Yichang and Wuhan, with PM_2.5 concentration prediction reduced by 20% and 16%, respectively. Ts scores for daily predictions of PM_2.5 and O₃ were 60.00% in Yichang and 69.23% in Wuhan. The regression prediction model can provide a scientific basis for the prediction of PM_2.5 and O₃ concentration and the control of compound pollution in Yichang and Wuhan cities which were affected by meteorological conditions.