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.

Abstract. Our knowledge is still poor regarding the response of the precipitation vertical structure to aerosols, partly due to the ignorance of precipitation occurring at different spatial scales. A total of 6 years of collocated ground-based PM10 and satellite-based (Tropical Rainfall Measuring Mission, TRMM) radar data, along with ERA-Interim reanalysis, are used in this study to investigate the aerosol effects on three localized rain regimes (shallow, stratiform, and convective rain) over the Pearl River Delta region of China. A subjective analysis method is proposed to discriminate between the localized and synoptic-scale precipitations based on weather composite charts where daily averaged wind field at 850 hPa is overlaid with the geopotential height at 500 hPa. In general, average rain rate tends to be greater under polluted conditions than under clean conditions. But such potential aerosol effects are regime dependent: as the atmosphere becomes slightly polluted (PM10≤38 µg m−3), the top 1 % radar reflectivity (Z) for all regimes initially increases, followed by continued increases and weak decreases for convective and stratiform/shallow rain regimes, respectively. As the atmosphere becomes much more polluted, such regime dependences of aerosol effects are more significant. From a perspective of the vertical Z structure, comparisons between polluted conditions (days with the highest third of PM10 concentration) and clean conditions (days with the lowest third of PM10 concentration) show that the convective rain regime exhibits a deeper and stronger Z pattern, whereas a much shallower and weaker Z pattern is observed for stratiform and shallow precipitation regimes. In particular, the top height of the 30 dBZ rain echo increases by ∼29 % (∼1.27 km) for the convective regime, but decreases by ∼10.8 % (∼0.47 km) for the stratiform regime. However, no noticeable changes are observed for the shallow precipitation regime. Impacts of meteorological factors are further studied on both rain top height (RTH) and the center of gravity of Z, including vertical velocity, vertical wind shear, convection available potential energy, and vertically integrated moisture flux divergence (MFD). The possible invigoration effect on convective precipitation seems dependent on wind shear, in good agreement with previous findings. Overall, the observed dependence of the precipitation vertical structure on ground-based PM10 supports the notion of aerosol invigoration or suppression effect on cold or warm rain and adds new insights into the nature of the complex interactions between aerosol and various localized precipitation regimes.

We analyzed the impact of aerosols on precipitation based on 3 years of 3-hourly observations made in heavily polluted eastern China. The probability of precipitation from different cloud types was calculated using International Satellite Cloud Climatology Project cloud data and gauge-based hourly precipitation data. Because deep convective clouds have the largest precipitation probability, the influence of aerosols on the precipitation from such clouds was studied in particular. Aerosol properties were taken from the Modern-Era Retrospective Analysis for Research and Applications Aerosol Reanalysis data set. As aerosol optical depth increased, rainfall amounts from deep convective clouds increased at first and then decreased. The descending part of the trend is likely due to the aerosol radiative effect. Downwelling solar radiative fluxes at the surface decreased as aerosol optical depth increased. The decrease in solar radiation led to a decrease in ground heat fluxes and convective available potential energy, which is unfavorable for development of convective clouds and precipitation. The tendencies for lower cloud top temperatures, lower cloud top pressures, and higher cloud optical depths as a response to larger aerosol optical depths suggest the invigoration effect. Vertical velocity, relative humidity, and air temperature from the National Centers for Environmental Prediction Climate Forecast System Reanalysis were sorted to help investigate if the trends are dependent on any environmental conditions. How dynamic and microphysical factors strengthen or mitigate the impact of aerosols on clouds and precipitation and more details about their interplay should be studied further using more observations and model simulations.