## 3 Results
### Effect of Tropical Cyclones on the Estimation of Extreme SWH  
In the previous sections, it was hypothesized that high PoT-based SWHs in summer were associated with typhoons. To clarify whether these are indeed related to typhoons, we classified all the spatial grids into two regions of typhoon and non-tyhoon by applying a limit to the number of typhoons (N = 10) in each bin. Figure 13a shows the histogram of the differences between PoT-derived extreme SWHs and the maximum SWH in the typhoon region with a frequency of greater than 10, corresponding to a cumulative percentage of typhoon passage frequency of 60% approximately as shown in Figure 1c,d. The differences between the 100-year return SWH obtained from the PoT analysis and the maximum SWHs showed positive values in nearly all regions (>99.5%) regardless of the number of  
Figure 12: Spatial distribution of seasonal extreme SWH (\(H_{s}^{100}\)) (m) estimated by the PoT method in (**a**) spring (March, April, and May), (**b**) summer (June, July, and August), (**c**) fall (September, October, and November), and (**d**) winter (December, January, and February).  
typhoons, which indicated that the characteristics of extreme conditions were appropriately reflected in the EVA (Figure 13a,d). The maximum count was found at approximately 4 m, which was similar to that of non-typhoon regions, as shown in Figure 13d. Although the maximum frequency appeared at a difference of the SWHs in both typhoon and non-typhoon regions, their occurrence months, with a high frequency of high SWHs within the upper 0.1%, differed as they appeared in August to October in the case of typhoon regions (Figure 13b) and in December to February in the case of non-typhoon regions (Figure 13e). In total, 56.6% of the typhoon regions appeared to present the maximum SWH between August and October, whereas 67.5% of the non-typhoon region showed the maximum value in winter. The PoT-derived SWHs showed a high frequency in fall (from September to November) in the typhoon region, while the maximum frequency appeared in winter (December to February) in the non-typhoon region, as shown in Figure 13c,f. The time of the maximum value of the extreme SWHs estimated by the PoT analysis was similar to that of the maximum SWH, which also demonstrated that the PoT analysis appropriately simulated the characteristics of extreme SWH in the Northwest Pacific.  
The distributions of SWHs showed considerable differences between the typhoon region and in the non-typhoon region. In order to investigate the characteristics of SWH distribution in the typhoon and non-typhoon region in more detail, two points representing the typhoon (128\({}^{\circ}\)E, 30\({}^{\circ}\)N) and non-typhoon (174\({}^{\circ}\)E, 52\({}^{\circ}\)N) region were selected, and the PDFs were determined by PoT analysis (Figure 14). In the selected point representing the typhoon region (128\({}^{\circ}\)E, 30\({}^{\circ}\)N), about 30 typhoons passed over the study period. As expected, the quantiles estimated by the PoT analysis showed relatively good agreement with the observed quantiles (Figure 14a). In addition, as shown in Figure 14b, the PoT analysis estimated appropriate extreme SWHs in the point representing the non-typhoon region. With the accumulation of satellite-observed SWH data over several decades, it  
Figure 13: Histograms of (**a**) differences between extreme SWHs using PoT and SWHs within upper 0.1%, (**b**) time (in month) of SWHs within upper 0.1%, (**c**) time of the maximum value of extreme SWHs using PoT in the typhoon region, with a number of typhoons greater than 10, and (**d–f**) the cases for the non-typhoon region with a number of typhoons of less than 10.  
can also be suggested that the PoT analysis can be used to estimate more reliable extreme SWHs in the Northwest Pacific.