## Data and Methodology  
### Data Preparation  
In this study, multiple sensors from A-Train are used to investigate the possible reasons for the dimming and brightening variations during the monsoon season (June-September) in South Asia from 2006 to 2015. The MYD08 and Level 2 aerosol layer products from MODIS and CALIPSO are used to describe the recent changes in aerosol loading in South Asia [16; 17]. The CloudSat 2B-CLDCLASS, 2B-CWC-RO, and 2B-FLXHR products are used to quantify the 3D changes in cloud macrophysical and microphysical characteristics and corresponding variable radiative effects of clouds [18]. Moreover, environmental conditions are obtained from the European Centre for Medium-Range Weather Forecast-AUXiliary analysis (ECMWF-AUX) product [19]. The radiative transfer model of BUGSrad and ECMWF-AUX are used to evaluate the 3D changes in water vapor amount and related radiative forcing. The CERES/Aqua product is considered for evaluating the surface radiation energy variations [20]. The specific information about all sensors, products, and variables used in this study are collected and provided in Table S1. We define the study domain within South Asia to restrict the effect of surrounding land and sea pixels on the results of this study, as illustrated in Figure S1. Moreover, aerosols detected by CALIPSO can be classified into the following six subtypes with high accuracy: clean marine, dust, polluted continental, clean continental, polluted dust, and smoke [21]. In this study, CALIPSO Level 2 aerosol layer products are used to describe the recent changes in aerosol loading and its mostly subtype component in South Asia. CALIPSO is highly sensitive to thin cirrus clouds but cannot easily penetrate optically thick layers with optical depths that exceed 3-5 [22; 23]. In this case, CALIPSO determines a no or low aerosol optical depth (AOD) at near-surface due to the complete attenuation of lidar signals. To exclude the effects of unpenetrated profiles for lidar, we calculate the average AOD from CALIPSO only, based on the scene in which the lidar penetrates the entire atmosphere. Furthermore, the 2B-FLXHR product is formed on the basis of the retrieved profiles of cloud macrophysical and microphysical properties from CloudSat, and is used to evaluate the radiative and heating rate forcing of 3D cloud variations. [PERSON], et al. [18] detected biases between the radiative data of 2B-FLXHR from CloudSat and those from CERES with monthly 5\({}^{\circ}\) means on the global scale. The biases of outgoing shortwave (SW) radiation, outgoing longwave (LW) radiation, surface shortwave radiation (SSR), and surface LW radiation are less than 5.5 (\(\%\)), 0.1 (<0.1\(\%\)), 16 (\(\%\)), and 13 W m\({}^{-2}\) (4\(\%\)), respectively. Moreover, the uncertainties in 2B-FLXHR fluxes decrease significantly for long time scale averages [18]. Therefore, the radiative forcing of clouds, derived from CloudSat, have been verified as credible, especially in large space and time scales [18; 24]. Strict selection procedures are implemented to control the quality of the data products, thereby ensuring credible conclusions. The cloud mask and radar reflectivity, from the CloudSat 2B-GEOPROF, are set to be more than 20 and \(-\)28 dBZ, respectively, and the quality flag from the 2B-CLDCLASS is identified as confidence to CloudSat [25]. We define the cloudy conditions for all products as the scenes in which the cloud mask from the CloudSat footprint exceeds 20, and the others are the clear-sky conditions. The probability of a false detection of clouds to CloudSat is less than 5\(\%\) in comparison with that by CALIPSO under this cloud screening method [25]. Data quality for CALIPSO is maintained by screening the cloud layer with a high degree of confidence (\(>\)90\(\%\)) [22]. We only use the radiative data from CERES with a sensor-viewing zenith angle less than 60\({}^{\circ}\) at the surface, to restrict the uncertainty from the satellite non-nadir point [20].