## Appendix A Statistical Metrics  
The first set of statistical tests was performed with the purpose of evaluating the differences, coherence, and correlation between the TMPA data and the IMERG data, both with 0.1\({}^{\circ}\) spatial resolution. These tests include the Shapiro-Wilk normality test [61]. This test rejects the hypothesis of normality when the respective \(p\)-value (denoted by \(p_{sw}\)) is less or equal to 0.05 (i.e., \(p_{sw}\leq 0.05\)). The Wilcoxon signed-ranked test [62] compares two dependent samples to determine if their populations have the same distribution by comparing their medians. The two samples show no differences and considerable dependency when the respective p-value (denoted by \(p_{w}\)) is greater than 0.05 (i.e., \(p_{w}>0.05\)). The Spearman correlation coefficient (denoted by \(R_{s}\)) determines the correspondence between two variables. If the two samples exhibit a perfect positive correlation, then \(R_{s}=1\). For a perfect negative correlation, \(R_{s}=-1\) and, for no correlation, \(R_{s}=0\). The null hypothesis (H\({}_{0}\)) that any correlation between the two variables due to chance is tested by calculating the Spearman test \(p\)-value (denoted by \(p_{s}\)). This test examines whether the rankings of each data set are similar (the relationship does not have to be linear). In this study, for \(p_{s}<0.01\), H\({}_{0}\) is very strongly rejected, for \(0.01\leq p_{s}<0.05\), H\({}_{0}\) is strongly rejected, for \(0.05\leq p_{s}<0.1\), the evidence for rejecting H\({}_{0}\) is weak and, for \(p_{s}\geq 0.1\), the evidence for rejecting H\({}_{0}\) is very weak.  
The second group of verification statistics was selected with the purpose of identifying the remote sensing product with higher compatibility to the in-situ gauges. A Spearman correlation coefficient test was applied between the rain gauge data and the TMPA (0.25\({}^{\circ}\)), TMPA (0.1\({}^{\circ}\)), and IMERG (0.1\({}^{\circ}\)) data, which were all collected between 2015 and 2018. This was done to determine the correlational strength between the remote sensing data and the benchmark. The verification statistics used here are the Root Mean Square Error (RMSE, Equation (A1)) and the Mean Absolute Error (MAE, Equation (A2)). A BIAS test (Equation(A3)) was also used [17; 54].  
\[\text{RMSE}=\sqrt{\frac{1}{n}\Sigma_{i=1}^{n}\big{(}P_{sat_{i}}-P_{gau_{i}} \big{)}^{2}}\] (A1)  
\[\text{MAE}=\frac{1}{n}\sum_{i=1}^{n}\big{|}P_{sat_{i}}-P_{gau_{i}}\big{|}\] (A2)  
\[\text{BIAS}=\frac{1}{n}\sum_{i=1}^{n}\big{(}P_{sat_{i}}-P_{gau_{i}}\big{)}\] (A3)In the above relationships, \(P_{sat}\) refers to satellite precipitation records, \(P_{gpu}\) represents the records derived from the in-situ rain gauges, and \(n\) is the number of samples.  
A third group of categorical statistics was used to verify the potential of the satellite products in detecting rainfall at various rainfall thresholds (i.e., 0.1, 1, and 10 mm). These are the Probability of Detection (POD, Equation (A4)), the False Alarm Ratio (FAR, Equation (A5)), and the Critical Success Index (CSI, Equation (A6)), calculated for each single event [17; 54].  
\[\text{POD}=\frac{\text{Hits}}{\text{Hits}+\text{Misses}}\] (A4)  
\[\text{FAR}=\frac{\text{false alarms}}{\text{Hits}+\text{false alarms}}\] (A5)  
\[\text{CSI}=\frac{\text{Hits}}{\text{Hits}+\text{false alarm}+\text{Misses}}\] (A6)  
Hits are defined as rain detected by both gauges and satellites and misses as rain observed by gauges but not detected by a satellite. False alarms were described as rain detected by satellites but not observed by ground rain gauges [54].