*3.1. Evaluation of GPM-Based Multitemporal Precipitation*

The execution of proposed downscaling methodology was first formulated through evaluating the precipitation response, e.g., each GPM-based multitemporal precipitation variable with respect to geospatial predictors at each low-resolution scale. Additionally, each investigated precipitation variable, e.g., the average monthly, the average annual (2001–2015), the average winter, the average spring, the average summer, the average autumn, the dry year (2001) and the wet year (2004) precipitation was plotted against each geospatial predictor at each upscaled resolution. Demonstration through scatter diagrams and polynomial regression (i.e., Figures S3–S5) described the relationship between precipitation variables and geospatial predictors, i.e., elevation, longitude and latitude at upscaled resolutions, respectively. Moreover, the R<sup>2</sup> values are shown in Table 1, wherein all precipitation variables showed strong response to latitude followed by longitude and elevation, respectively. Furthermore, for the individual precipitation variables, the average spring followed by the dry year (2001) and the wet year (2004) precipitation showed a higher relationship with geospatial predictors, respectively. Apart from geospatial predictors, the highest R2 for upscaled resolutions was observed at 1.0◦ and 0.75◦, respectively.


**Table 1.** Output of model fitting between the multitemporal GPM variables and geospatial predictors.

Note: SR stands for the scaled resolution, GP. for geospatial predictors, M for the average monthly, A for the average annual (2001–2015), Wn for the average winter, Sp for the average spring, Su for the average summer, Au for the average autumn, Wet-y for the wet year (2004), Dry-y for the dry year (2001) precipitation, respectively.
