Descriptive Statistics

Various statistical parameters are used to verify the proposed methodology by indicating the perfect score and range for each statistical metric, i.e., R2, RMSE and the bias (B), which are expressed in Equations (11) and (12):

$$RMSE = \left(\sum\_{i} ^{n} (Ob\_{i} - Pr\_{i})^{2} \Big/ n\right)^{\frac{1}{2}} \tag{11}$$

$$B = \frac{\sum\_{i=1}^{n} Pr\_i}{\sum\_{i=1}^{n} Ob\_i} - 1 \tag{12}$$

where *Obi* is the observed variable, *Pri* is the predicted variable, and *n* is the number of observations.

### 2.3.3. EDBF Algorithm

Based on polynomial regression outputs, the most influencing geospatial predictor that predicts multitemporal precipitation variables at each low-resolution scale is considered for further evaluation through EDBF algorithm. In this research, the developed methodology is based on the earlier work of [57,58]. The execution of EDBF algorithm is shown in Figure S2. Based on calculated *r* values, the process starts through randomly generating initial weight vector *W*, which by substituting into Equation (13) obtains *WTP*:

$$\text{WTP} = w\_{\text{M}} \times \text{M} + w\_{\text{An}} \times \text{An} + w\_{\text{W}} \times \text{W} + w\_{\text{Sp}} \times \text{Sp} + w\_{\text{Su}} \times \text{Su} + w\_{\text{Au}} \times \text{Au} + w\_{\text{Wt}} \times \text{Wt} + w\_{\text{Dr}} \times D\text{r} \tag{13}$$

where *WTP* is the weighted precipitation, *W* = {*wM*, *wAn*, *wW*, *wSp*, *wSu*, *wAu*, *wWt*, *wDr* } corresponds to the weight values (Equation (14)), and vector *M*, *An*, *W*, *Sp*, *Su*, *Au*, *Wt* and *Dr* corresponds to each of the eight precipitation variables, i.e., the average monthly, the average annual, the average winter, the average spring, the average summer, the average autumn, the wet year (2004) and the dry year (2001) precipitation, respectively. Additionally, vector *Res*0.25, *Res*0.75, *Res*0.50, *Res*1.0, *Res*1.25 and *Res*1.50 corresponds to each low-resolution scale, e.g., 0.25◦, 0.5◦, 0.75◦, 1.0◦, 1.25◦ and 1.50◦, respectively:

$$w\_M + w\_{A\nu} + w\_W + w\_{Sp} + w\_{Su} + w\_{A\nu} + w\_{Wl} + w\_{Dr} = 1\tag{14}$$

Subsequently, the correlation coefficient *RWTP*−*Res*0.25 , *RWTP*−*Res*0.50 , *RWTP*−*Res*0.75 , *RWTP*−*Res*1.0 , *RWTP*−*Res*1.25 and *RWTP*−*Res*1.50 between the *WTP* and vector *Res*0.25, *Res*0.50,*Res*0.75, *Res*1.0, *Res*1.25 and *Res*1.50 is calculated, respectively. In addition, EDBF algorithm is run to iteratively optimize W to obtain an accurate weight vector *Wt*, where *t* represents the number of iterations. Moreover, a relationship (Equation (5)) between *WTP* and geospatial predictor at vector *Res*0.25, *Res*0.50, *Res*1.0, *Res*1.25 and *Res*1.50 is evaluated, respectively. Hereafter, the best predicted resolution vector is used in the downscaling process. Similarly, using Equation (5), the same process is repeated for the high-resolution vector *Res*0.05, i.e., 0.05◦ scale resolution.
