3.2.1. Low-Resolution Weighted Precipitation

Based on initial regression analysis, the most influencing geospatial predictor, namely latitude, was selected to predict the weighted precipitation from the multitemporal precipitation variables via EDBF algorithm. In this regard, *r* values were calculated (Figure 3a), and used as the basis function to randomly assign initial weight value to each precipitation variable. The reason for negative *r* values is the existence of a negative relationship between latitude and precipitation variables. Subsequently, the chi-square (χ2) test was employed to evaluate the relationship between precipitation variables at each low-resolution scale for assigned weight values. The χ<sup>2</sup> *calculated* and <sup>χ</sup><sup>2</sup> *tabulated* values with 35 degrees of freedom at the significance level (α = 0.05) were 5.267 and 49.802, respectively. Based on statistical results, the χ<sup>2</sup> *calculated*<sup>&</sup>lt; <sup>χ</sup><sup>2</sup> *tabulated*, thus, the null hypothesis was accepted and rejected the alternative hypothesis. Moreover, it is stated that weight values assigned to precipitation variables were significantly not different. The details can be found in Table S1.

Furthermore, the correlation between precipitation variables and the low-resolution scales was analyzed and is shown in Figure 3b, wherein it showed that the dry year (2001) followed by the average spring, the wet year (2004) and the average summer precipitation are the most influencing variables. As far the scaled resolutions are concerned, 1.0◦ followed by 0.75◦ resolution had higher impacts.

Onward, the precipitation data was evaluated through EDBF algorithm, and the number of iterations was set to 3 <sup>×</sup> 104. Figure 4 demonstrates the iteration wise statistics at each upscaled resolution, in which Figure 4a,d,g,j,m,p show weight values, Figure 4b,e,h,k,n,q show *r* values, and Figure 4c,f,i,l,o,r show the comparison between weight and *r* values which were iteratively generated by the algorithm itself. To investigate weight values, it was observed that lots of discrepancies exist in the convergence of investigated variables (e.g., Figure 4a,d,g,j,m,p), and the convergence showed stabilization onward 2 <sup>×</sup> 104 iterations. In Figure 4a,d,g, the dry year (2001), the average spring and the average autumn; Figure 4j, the dry year (2001), the average spring and the wet year (2004); Figure 4m,p, the dry year (2001), the average autumn and the average spring, respectively, showed higher weight values from the beginning until the last iteration. As far *r* values are concerned, uncertainty in initial iterations was observed as shown in Figure 4b,e,h,k,n,q, and the convergence

showed stabilization onward 1 <sup>×</sup> 104 iterations. Likewise, it was also observed that *<sup>r</sup>* values drastically decreased before the stabilization of convergence.

**Figure 3.** Selection of initial input parameters for EDBF) algorithm to predict the weighted precipitation (**a**) calculated *r* values, and (**b**) the correlation between precipitation variables and the low-resolution scales for assigned weight values.

In addition, the weighted *r* value predicted by EDBF algorithm was higher as compared to the calculated *r* value for each precipitation variable, as shown in Figure 4c,f,i,l,o,r. The highest weighted *r* was predicted at 1.0◦ (−0.891) followed by 0.75◦ and 1.25◦ (−0.889), 0.25◦ (−0.880), and 0.50◦ and 1.50◦ (−0.867), respectively. Nevertheless, the final weight value predicted at all upscaled resolutions was same, e.g., equal to 1, but weighted response towards precipitation variables was not similar. It can clearly be observed that the highest weighted response was given to the dry year (2001) (Figure 4l,i,c,f,r,o) followed by the average spring (Figure 4l,i,o,c,f,r), the average autumn (Figure 4f,r,o,c,i), and the wet year (2004) (Figure 4l), respectively. Finally, the relationship between latitude and the weighted precipitation predicted by EDBF algorithm was shown through scatter plots in Figure 5a–f. In contrast to earlier plots, i.e., the exitance of polynomial relationship between precipitation variables and geospatial predictors, here, the linear relationship was observed. Moreover, the R<sup>2</sup> between latitude and the weighted precipitation at each upscaled resolution was increased. The higher R2 was observed at 1.0◦, 1.25◦,0.75◦ resolutions, respectively. Overall, R2 was higher than 0.75.

**Figure 4.** Execution of EDBF algorithm at different low-resolution scales, (**a**,**d**,**g**,**j**,**m**,**p**) iteratively estimated weighted values, (**b**,**e**,**h**,**k**,**n**,**q**) iteratively estimated *r* values, and (**c**,**f**,**i**,**l**,**o**,**r**) the comparison between assigned weights and estimated *r* values at 0.25◦, 0.50◦, 0.75◦, 1.0◦, 1.25◦, 1.50◦ resolutions, respectively.

**Figure 5.** Scaled wise relationship between the weighted precipitation and (**a**) latitude at 0.25◦ resolution; (**b**) latitude at 0.50◦ resolution; (**c**) latitude at 0.75◦ resolution; (**d**) latitude at 1.0◦ resolution; (**e**) latitude at 1.25◦ resolution; (**f**) latitude at 1.50◦ resolution; (**g**) the dry year (2001) precipitation at 0.75◦ resolution; (**h**) the wet year (2006) precipitation at 0.75◦; and (**i**) the average annual (2001–2015) precipitation at 0.75◦, respectively.
