*3.2. Day-to-Day and Interannual Variation*

Figure 2a shows the time series of the 5-day running mean for the precipitation area-averaged over Taiwan during the summers of 2014–2017. Using the time series in Figure 2a, two statistical scores, Tcorr and RMSE, between the CWB data and the SPPs, were then calculated for providing evaluation evidence. As seen in Figure 2b, all SPPs have similar values of Tcorr (~0.9), but the lowest RMSE is observed in IMERG6 and the highest RMSE is observed in TRMM7. Overall, the RMSE of IMERG6 (~7.6 mm·d−1) is approximately 12.6% lower [= (8.7 <sup>−</sup> 7.6)/8.7 <sup>×</sup> 100%] than the RMSE of TRMM7 (~8.7 mm·d<sup>−</sup>1).

**Figure 2.** (**a**) Time series of 5-day running averaged precipitation over Taiwan, during the summers of 2014–2017, extracted from the CWB data and the four SPPs. The color symbols are described in (**b**). (**b**) Tcorr (i.e., temporal correlation) and RMSE between the time series of CWB data and SPPs in (**a**). (**c**) Grid-to-grid Tcorr between the time series of CWB data and SPPs. (**d**) Grid-to-grid RMSE between the time series of CWB data and SPPs. Here, the sample size for calculating Tcorr and RMSE is 368 days from summers of 2014–2017. In (**c**,**d**), the values pass the 99% significant test are marked by dots.

Additionally, as noted from Figure 2c, which shows the spatial distribution of grid-to-grid Tcorr for the comparison between the SPPs and the CWB data, IMERG6 has more areas with larger values of Tcorr (e.g., >0.8). Moreover, even though the related spatial distributions of grid-to-grid RMSE in Figure 2d do not show too much difference among the performance of the four SPPs, IMERG6 is still the one having more areas with smaller values of RMSE (e.g., <7.5 mm·d<sup>−</sup>1). These features suggest that the performance of IMERG6 is overall better than the other SPPs in depicting the day-to-day variations of precipitation over Taiwan.

Recall, Derin et al. [15] indicated that IMERG6 performed worse than GSMaP7 and IMERG5 in capturing the daily precipitation formation over western Taiwan during 2014–2015. Consistent with Derin et al. [15], one can note from Figure 2c that GSMaP7 and IMERG5 did perform better than IMERG6 in some coastal regions of southwest Taiwan, even though the time periods used for the comparison are different in Figure 2c and Derin et al. [15]. However, in contrast to Derin et al. [15], we would like to call attention that when focused on the daily precipitation formation over whole

Taiwan during the summers of 2014–2017, the performance of IMERG6 is overall better than GSMaP7 and IMERG5.

Next, statistical evidence is provided for evaluating the capabilities of IMERG6 and TRMM7 to depict the variations of daily precipitation events during the summers of 2000–2017. Figure 3a shows the distribution of the occurrence frequency of precipitation events at various ranges of intensity (units: mm·d<sup>−</sup>1). From Figure 3a, we note that both TRMM7 and IMERG6 tend to underestimate the occurrence frequency of precipitation events at most ranges of intensity. Despite that, the performance of IMERG6 is overall better (i.e., more close to the CWB data) than TRMM7, in particularly for capturing the occurrence frequency of light precipitation events (see right top panel of Figure 3a).

**Figure 3.** (**a**) Histograms of the frequency of occurrence as a function of daily precipitation intensity (bin size is 1 mm·d<sup>−</sup>1) in Taiwan during the summers of 2000–2017. Inset plots represent the frequency (in %) of light (0.1–5 mm·d<sup>−</sup>1), moderate (5–20 mm·d<sup>−</sup>1), and heavy precipitation (>20 mm·d<sup>−</sup>1) events. The method used for generating (**a**) follows Sun et al. [43]. (**b**) and (**c**) is the value of TS and BS (explained in Section 2), respectively, for the comparison between CWB data and two SPPs (IMERG6 and TRMM7) during the summers of 2000–2017. The sample size used here is 92 (days per JJA) × 18 (JJAs) × 392 (grid points per day) = 649152 grid points.

Moreover, two other statistical scores, TS (i.e., threat score) and BS (i.e., bias score), are calculated for representing the skill of SPPs in quantitative precipitation estimations. It can be noted in Figure 3b that the value of TS in IMERG6 is higher than in TRMM7 over all ranges of precipitation threshold. As higher TS values indicate better performance [36], Figure 3b again suggests that IMERG6 outperforms TRMM7. On the other hand, both IMERG6 and TRMM7 have values of BS < 1 over all ranges of precipitation threshold. This implies that precipitation events in both IMERG6 and TRMM7 occurred less often than that in the CWB data [36]. However, relative to TRMM7, IMERG6 still has a BS value closer to the ideal value of 1.

Figure 4a shows the interannual variation of summer precipitation, area-averaged over Taiwan, estimated by the CWB data and the SPPs. Consistent with Figure 3a, the variations of IMERG6

(TRMM7) is more (less) close to the CWB data during the summers of 2000–2017. The spatial distribution of grid-to-grid Tcorr (RMSE) between the SPPs and the CWB data are further conducted in Figure 4b (Figure 4c) for evaluating the performance of TRMM7 and IMERG6. It is noted that IMERG6 (TRMM7) has more (less) areas with Tcorr <sup>&</sup>gt; 0.8 and RMSE <sup>&</sup>lt; 5 mm·d<sup>−</sup>1, suggesting again that IMERG6 outperforms TRMM7.

**Figure 4.** (**a**) Time series of the mean precipitation for the summers of 2000–2017, area-averaged over Taiwan, as estimated by the CWB data and SPPs. (**b**) and (**c**) is the related grid-to-grid Tcorr and RMSE, respectively, between the CWB data and the selected SPPs: TRMM7 and IMERG6. Here, the sample size for calculating Tcorr and RMSE is 18 JJAs. In (**b**,**c**), the values pass the 99% significant test are marked by dots.

Also, for the performance of IMERG5 and GSMaP7 in illustrating the interannual variation of summer precipitation over Taiwan, some information is given in Figure 4a. It was found that even when focused on the summers of 2014–2017, IMERG6 (TRMM7) is still more (less) close to the CWB data, as compared to the other SPPs.
