We develop a novel, dual-CTM bias correction framework to attribute summertime PM_2.5 and ozone changes over eastern China during 2015–2024. The framework substantially reduces the CTM biases and reconciles the inter-model discrepancies in the attribution. Emission reductions dominate both the PM_2.5 decline and ozone increase, but there is a marked transition of their role after 2019. Persistent unfavorable meteorological conditions contribute to ozone increase especially before 2019.

We examine the impact of diurnally varying African biomass burning (BB) emissions on tropospheric ozone using GEOS-Chem simulations with a high-resolution satellite-derived emission inventory. Compared to coarser temporal resolutions, incorporating diurnal variations leads to significant changes in surface ozone and atmospheric oxidation capacity. Our findings highlight the importance of accurately representing BB emission timing in chemical transport models to improve ozone predictions.