Aerosols have significant and complex impacts on regional climate in East Asia. Cloud‐aerosol‐precipitation interactions (CAPI) remain most challenging in climate studies. The quantitative understanding of CAPI requires good knowledge of aerosols, ranging from their formation, composition, transport, and their radiative, hygroscopic, and microphysical properties. A comprehensive review is presented here centered on the CAPI based chiefly, but not limited to, publications in the special section named EAST‐AIRcpc concerning (1) observations of aerosol loading and properties, (2) relationships between aerosols and meteorological variables affecting CAPI, (3) mechanisms behind CAPI, and (4) quantification of CAPI and their impact on climate. Heavy aerosol loading in East Asia has significant radiative effects by reducing surface radiation, increasing the air temperature, and lowering the boundary layer height. A key factor is aerosol absorption, which is particularly strong in central China. This absorption can have a wide range of impacts such as creating an imbalance of aerosol radiative forcing at the top and bottom of the atmosphere, leading to inconsistent retrievals of cloud variables from space‐borne and ground‐based instruments. Aerosol radiative forcing can delay or suppress the initiation and development of convective clouds whose microphysics can be further altered by the microphysical effect of aerosols. For the same cloud thickness, the likelihood of precipitation is influenced by aerosols: suppressing light rain and enhancing heavy rain, delaying but intensifying thunderstorms, and reducing the onset of isolated showers in most parts of China. Rainfall has become more inhomogeneous and more extreme in the heavily polluted urban regions.

Aerosol optical quantities have been widely employed as the proxy variables of cloud condensation nuclei concentration to study aerosol indirect effects (AIE). Due to the aerosol swelling effect, cloud condensation nuclei and aerosol optical quantities do not vary in harmony, leading to a bias in the estimation of the AIE. To identify and quantify this artifact, we employ extensive measurements of aerosol and cloud properties made at four sites in different continents that have distinct aerosol properties in terms of size and composition. One‐unit enhancement in aerosol scattering coefficient by swelling effect is found to lead to a systematic underestimation of the first indirect effect (FIE) by about 23% that can result in an underestimation in the FIE‐related radiative forcing by several W/m² depending on aerosol properties and relative humidity. This likely contributes significantly to the systematic difference between satellite‐based estimates of the FIE and those simulated by general circulation models. Recommendations are made to make more sound comparisons of the AIE estimated from observations and model simulations.

Satellite-derived aerosol optical depths (AODs) have been widely used to estimate surface fine particulate matter (PM_2.5) concentrations over areas that do not have PM_2.5 monitoring sites. To date, most studies have focused on estimating daily PM_2.5 concentrations using polar-orbiting satellite data (e.g., from the Moderate Resolution Imaging Spectroradiometer), which are inadequate for understanding the evolution of PM_2.5 distributions. This study estimates hourly PM_2.5 concentrations from Himawari AOD and meteorological parameters using an ensemble learning model. We analyzed the spatial agglomeration patterns of the estimated PM_2.5 concentrations over central East China. The estimated PM_2.5 concentrations agree well with ground-based data with an overall cross-validated coefficient of determination of 0.86 and a root-mean-square error of 17.3 μg m⁻³. Satellite-estimated PM_2.5 concentrations over central East China display a north-to-south decreasing gradient with the highest concentration in winter and the lowest concentration in summer. Diurnally, concentrations are higher in the morning and lower in the afternoon. PM_2.5 concentrations exhibit a significant spatial agglomeration effect in central East China. The errors in AOD do not necessarily affect the retrieval accuracy of PM_2.5 proportionally, especially if the error is systematic. High-frequency spatiotemporal PM_2.5 variations can improve our understanding of the formation and transportation processes of regional pollution episodes.