Atmospheric visibility profoundly impacts daily life, and accurate prediction is crucial, particularly in conditions of low visibility characterized by high aerosol loading and humidity. This study employed the WRF‐Chem model to simulate a severe wintertime haze pollution episode that transpired from January 17 to 19, 2010, in Central‐East China (112–122° E, 34–42° N). The results reveal that excluding aerosol–meteorology interactions led to underestimated PM_2.5 concentrations and relative humidity in comparison with ground‐based measurement data, accompanied by a significant overestimation of visibility. Aerosols can engage with meteorological elements, particularly humidity, resulting in positive feedback. Upon considering these feedback interactions, the simulation results showed an increase of 5.17% and 1.99% in PM_2.5 concentration and relative humidity, respectively, compared with the original simulation. This adjustment narrowed the bias between simulated and measured data. The overestimation of simulated visibility was reduced by 16% and 25% for the entire study period and the severe haze pollution period, respectively. These findings underscore the vital role of incorporating aerosol–meteorology interactions in visibility simulations using the WRF‐Chem model. Notably, the inclusion of aerosol–meteorological feedback significantly enhances the accuracy of visibility predictions, particularly during heavily polluted periods.

Aerosols can interact with other meteorological variables in the air via aerosol–radiation or aerosol–cloud interactions (ARIs/ACIs), thus affecting the concentrations of particle pollutants and ozone. The online-coupled model WRF-Chem was applied to simulate the changes in the PM_2.5 (particulate matter less than 2.5 μm in aerodynamic diameter) and ozone concentrations that are caused by these mechanisms in China by conducting three parallel sensitivity tests. In each case, availabilities of aerosol–radiation interactions and aerosol–cloud interactions were set differently in order to distinguish each pathway. Partial correlation coefficients were also analyzed using statistical tools. As suggested by the results, the ARIs reduced ground air temperature, wind speed, and planetary boundary height while increasing relative humidity in most places. Consequently, the ozone concentration in the corresponding region declined by 4%, with a rise in the local annual mean PM_2.5 concentration by approximately 12 μm/m³. The positive feedback of the PM_2.5 concentration via ACIs was also found in some city clusters across China, despite the overall enhancement value via ACIs being merely around a quarter to half that via ARIs. The change in ozone concentration via ACIs exhibited different trends. The ozone concentration level increased via ACIs, which can be attributed to the drier air in the south and the diminished solar radiation that is received in central and northern China. The correlation coefficient suggests that the suppression in the planetary boundary layer is the most significant factor for the increase in PM_2.5 followed by the rise in moisture required for hygroscopic growth. Ozone showed a significant correlation with NO₂, while oxidation rates and radiation variance were also shown to be vitally important.