Severe air pollution events accompanied by high PM_2.5 concentrations have been repeatedly observed in Middle-Eastern China since 2013 and decreased in recent years. The reason for this caused widespread attention. The month of January was selected to represent the winter season annual changes in the winter PM_2.5 and meteorological conditions—including the upper-air meridional circulation index (MCI), winds at 700 and 850 hPa levels and surface meteorology—from 2013 to 2019. These conditions were analyzed to study the contribution of meteorology changing to the annual PM_2.5 changing on the regional scale. Results show that, based on values of upper-level MCI, the years 2014, 2015, 2017, and 2019 were defined as meteorology-haze years and the years 2016 and 2018 were defined as meteorology-clean years. A change in meteorological conditions may lead to a 26% change in PM_2.5 concentration between 2014 and 2013 (two meteorology-haze years) and 16–20% changes in PM_2.5 concentration between meteorology-haze years and meteorology-clean years. Changes in pollutant emissions may cause 21–47% changes in PM_2.5 concentration between each two meteorology-haze years. A comparison of two meteorology-clean years and pollutant emissions in 2018 may be reduced by 40% compared with 2016. Overall, changes in emissions had a greater influence on changes in PM_2.5 compared with meteorological conditions.

In the present study, the Morrison double-moment cloud microphysics scheme including mass and droplet number concentration of water and ice clouds is implemented into the Chinese mesoscale version of the Global/Regional Assimilation and Prediction System (GRAPES_Meso). Sensitivity experiments of different cloud condensation nuclei (CCN) values are conducted to study the impacts of CCN on cloud microphysical processes and radiation processes in East China. The model evaluation shows that the simulated cloud liquid water path (CLWP) is consistent with that of the National Center for Environment Prediction (NCEP) reanalysis, and the cloud optical depth (COD) and effective radius of cloud water (Rc) are in agreement with those of the Moderate Resolution Imaging Spectroradiometer (MODIS) datasets both in regional distribution and magnitudes. These comparisons illustrate the effectiveness of the Morrison scheme for the cloud processes in East China. For the study period of 8 to 12 October 2017, the sensitivity experiments show that with initial CCN number concentration (CCN₀) increasing from 10 to 3000 cm⁻³, the maximum value of daily average Rc decreases by about 63%, which leads to a decrease of cloud-rain conversion rate. Moreover, the maximum value of daily average mixing ratio of cloud water (q_c) increases by 133%, the maximum value of daily average mixing ratio of rain (q_r) decreases by 44%, and the maximum value of daily average CLWP and COD increase by 100% and 150%, respectively. This results in about 65% increasing of the maximum value of daily average cloud downward shortwave radiative forcing (CDSRF) when CCN₀ increases from 10 cm⁻³ to 3000 cm⁻³. The study indicates the important impacts of CCN on cloud properties and radiation effects.