Severe fine particulate matter (PM<sub>2.5</sub>) and ozone pollution pose major air quality concerns in China. Chemical transport models (CTMs) are widely used to attribute their long-term trends to anthropogenic emissions and meteorological variabilities, but inherent uncertainties often yield inconsistent results across models. Here, we develop a bias correction framework that integrates a multi-layer perceptron (MLP) neural network to jointly constrain PM<sub>2.5</sub> and ozone attributions simulated by two independent CTMs, CMAQ and GEOS-Chem, over eastern China during 2015<span>–</span>2024. This framework improves simulated pollutant concentrations, spatial distributions, and interannual variabilities, providing a robust foundation for attribution analysis. During 2015<span>–</span>2024, anthropogenic emission changes accounted for 69<span>–</span>83 % of the observed PM<sub>2.5</sub> decline and 56<span>–</span>63 % of ozone changes across both corrected models, with meteorological variabilities contributing the remainder. Emission changes dominate the PM<sub>2.5</sub> decreases in 2015<span>–</span>2019, but the benefits weaken during 2019<span>–</span>2024 despite continued declines in major PM<sub>2.5</sub> precursors. For ozone, anthropogenic emissions drive increases during 2015<span>–</span>2019, but become effective for mitigating ozone thereafter. Meteorological variabilities exacerbate ozone increase in 2015<span>–</span>2019 but exert smaller impacts in later years. Crucially, the MLP correction reduces the inter-model attribution discrepancies from 136 % to 14 % for PM<sub>2.5</sub> and from 19 % to 8 % for ozone. These findings confirm the dominant role of emission changes in decadal (2015<span>–</span>2024) trends in summertime PM<sub>2.5</sub> and ozone in China, and underscore that sustained, coordinated emission reductions remain essential for mitigating both pollutants. Furthermore, they highlight the value of coupling state-of-the-art, mechanism-based CTMs with machine learning to reliably attribute long-term air quality trends.