3.3.1. Comparison between the Weighted and the Original Multitemporal Precipitation Variables

To compare the EDBF-based weighted precipitation with the GPM-based multitemporal precipitation variables, a linear regression model was established at all upscaled resolutions, e.g., 0.25◦, 0.5◦, 0.75◦, 1.0◦, 1.25◦ and 1.50◦. The efficiency comparison was established using three statistical metrics, i.e., R2, RMSE, and the bias (B). The results are shown in Table 2 (e.g., 0.75◦ resolution) and Table S2 (0.25◦, 0.5◦, 1.0◦, 1.25◦ and 1.50◦), respectively. From the tabulated results, it was observed that for the achieved R2 value, the weighted precipitation outperformed all multitemporal variables at all upscaled resolutions. The highest R2 value of 0.794 was observed at 1.0◦ followed by 0.792 at 0.75◦ resolution, respectively. Also, for the achieved RMSE value, the weighted precipitation outperformed the annual precipitation variables, such as the average annual (2001–2015), the wet year (2004) and the dry year (2001) precipitation, whereas it underperformed compared to the seasonal, e.g., the average winter, the average spring, the average summer and the average autumn precipitation, and the monthly precipitation variables. The lowest RMSE value (i.e., at all upscaled resolutions) was observed for the average monthly precipitation. Moreover, the observed bias for the two precipitation datasets, e.g., the weighted precipitation and the multitemporal precipitation variables, was almost reaching zero. In addition, both precipitation datasets were also compared at the original 0.1◦ resolution as shown in Table 3. The tabulated results revealed the same outcome as in Table 2, wherein the best correlation (R2) was observed between latitude and the weighted precipitation, and it outperformed all multitemporal precipitation variables. Similarly, for the achieved RMSE value, it outperformed the annual precipitation variables and underperformed compared to the seasonal and the monthly variables. As a whole, the observed output at each statistical parameter for each precipitation variable was slightly reduced from lower to higher (e.g., from Table 2 to Table 3) resolution.


**Table 2.** Comparison between the weighted precipitation and the multitemporal precipitation variables at 0.75◦ resolution.

**Table 3.** Comparison between the weighted precipitation and multitemporal precipitation variables original 0.1◦ resolution.


3.3.2. Verification of the Weighted Precipitation with Neutral Variables

The weighted precipitation was further evaluated by comparing with neutral variables which were not used during the prediction of EDBF-based weighted precipitation. In this regard, the precipitation variables from two different datasets, such as the TRMM and the GPM, were used for the verification of weighted precipitation. The GPM dataset used during verification comprised of the annual 2006 (Figure 2j) and 2012 (Figure 2k) precipitation, whereas the TRMM dataset comprised of the annual 2001, 2006, 2012 and the average annual (2001–2015) (Figure 2l–o) precipitation, respectively. The verification of weighted precipitation though the GPM data was evaluated by extracting precipitation at the original 0.1◦ resolution, whereas through the TRMM data, it was evaluated at the original 0.25◦ resolution. The verification results are shown in Table 4. The weighted precipitation outperformed both, as can be observed by comparing the datasets, by achieving a higher R<sup>2</sup> value of 0.776 at 0.25◦ resolution and 0.772 at 0.1◦ resolution as compared to the TRMM and the GPM-based precipitation, respectively. Subsequently, the weighted precipitation also produced lower RMSE, e.g., 133.37 (0.25◦ resolution) and 141.113 (0.1◦ resolution) as compared to the TRMM- and the GPM-based precipitation, respectively. Apart from that, the observed bias almost reached zero for all variables, wherein the weighted precipitation showed positive bias, while the TRMM and the GPM precipitation showed negative bias.


**Table 4.** Comparison between the weighted precipitation and neutral precipitation variables.
