## Data and Methodology
### Methodology  
We evaluated the total changes of aerosol, cloud, and water vapor, as well as their impacts on solar radiation during the monsoon season in South Asia Therefore, we collocated all observations at a resolution of \(1^{\circ}\times 1^{\circ}\) and ignored the different resolutions of multiple sensors. For the method of spatial averages, we firstly obtained the spatial distribution of the temporal average within a \(1^{\circ}\times 1^{\circ}\) grid during the monsoon season. We then determined the spatial average based on the above temporal average in the entire South Asia. For the spatial patterns of temporal changes, we identified the results of the linear temporal changes in atmospheric conditions during monsoon season from 2006 to 2015. The surface solar radiation was analyzed in a relative sense with variable aerosols, clouds, and water vapor. Thus, this study mostly focuses on the relative changes in surface solar radiation but less on the calculated absolute values, which are importantly affected by different detection methods, sensitivities, and retrieval algorithms of sensors. The cloud radiative effect (CRE) is widely used to quantify the degree of cloud-radiation interactions [24]. The present study is based on previous studies [28; 29; 30] in which the CRE in all-sky conditions is the net (down minus up) flux difference between the all- and clear-sky conditions at the atmosphere, surface, or TOA, as expressed in Equation (1):  
\[CRE\;=\;\left(\;F^{\downarrow}\;-\;F^{\uparrow}\;\right)_{All-sky}\;-\;\left( \;F^{\downarrow}\;-\;F^{\uparrow}\;\right)_{Clear-sky} \tag{1}\]  
where \(F^{\downarrow}\) and \(F^{\uparrow}\) are the downwelling and upwelling radiative fluxes at the TOA and surface, respectively. This study aims to quantify the effect of cloud variations at the TOA and surface fluxes. However, the CRE inferred from CERES is constantly disturbed by water vapor, aerosol, and the limited capability of combined MODIS to detect thin cirrus, mixed-phase clouds, and broken clouds [16]. Furthermore, CERES insufficiently considers the effect of multi-layer clouds [31]. Therefore, this study uses CloudSat 2B-FLXHR in evaluating the effects of cloud variations on radiation at the TOA and surface. Moreover, this study uses precipitable water (PW) variation to describe the column changes of water vapor in the atmosphere. PW is the total water vapor in the atmospheric column, which is calculated based on the data on the environmental parameters from the ECMWF-AUX product, as defined in Equation (2):  
\[PW\;=\;\frac{1}{g_{0}}\;\int_{P_{TOA}}^{P_{Surface}}\;q(P)dP \tag{2}\]  
where \(g_{0}=9.8\) m s\({}^{-2}\) is the standard acceleration caused by gravity at the mean sea level (m.s.l. ), \(P\) is the atmospheric pressure, and \(q(P)\) is the specific humidity of air that exists as a function of the atmospheric pressure [32]. The PW data from the ECMWF-AUX are verified with a bias of approximately \(-1\) mm (depending on water vapor amount) compared with the observations from 21 Global Position System and 14 radiosonde stations [32]. Therefore, the PW data derived from the ECMWF-AUX can adequately indicate the changes in water vapor amount found in South Asia. We accurately evaluated the effect of water vapor variations on solar radiation via BUGSrad. We used the environmental parameters, including temperature, pressure, specific humidity and cloud mask, provided by the CloudSat ECMWF-AUX products as the input for BUGSrad. Then, we varied only the average water vapor amount during the monsoon season, and other environmental parameters were fixed as the inter-annual average values to exclude the effect of other factors. We directly used the averaged environmental parameters for the radiative transfer model during the monsoon season to evaluate the radiative effect of water vapor by considering the quantity of the calculation.