## 3 Results and Discussion
### Estimation and Verification of Extreme Wind Speed
The extreme wind speeds for Guangzhou across different return periods, as predicted via empirical, Weibull, and Gumbel distributions, are summarized in Table 4. The Kolmogorov-Smirnov (KS) test results are presented in Table 5 and Figure 8. Only the Gumbel distribution was statistically validated through the Kolmogorov-Smirnov (KS) test, confirming its superior applicability for extreme wind speed modeling in Guangzhou. Notably, the wind speed estimates derived from all three distributions (empirical, Weibull, and Gumbel) across various return periods were consistently lower than the code-specified values, revealing the conservative nature of current regulatory standards in structural wind load design. Notably, similar findings have been reported in other regions, suggesting that the conservative nature of design wind speeds may be a common feature in international standards. For example, postevent investigations by [PERSON] and [PERSON] [42] in Australia revealed that the maximum wind speeds observed during cyclones Yasi, Debbie, and Seroja were typically between 80% and 90% of the design wind speeds specified in AS/NZS 1170.2 [10]; however, structural damage was generally limited to construction deficiencies rather than wind speeds exceeding design levels. In India, [PERSON] and [PERSON] [43] reported that, for cities along the eastern coastline, estimated 50-year return period cyclone wind speeds were significantly lower than those prescribed by the national code. Similarly, in China, [PERSON], [PERSON], [PERSON], [PERSON], [PERSON] and [PERSON] [17] reported that the wind speeds estimated for several southern coastal cities were lower than the current code recommendations, likely because of the influence of a mixed wind climate in the historical data used to define the design values. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Return Period (Year)** & **10** & **30** & **50** & **100** & **200** \\ \hline Weibull (m/s) & 18.02 & 21.15 & 22.39 & 23.92 & 25.32 \\ Gumbel (m/s) & 17.32 & 21.50 & 23.40 & 25.96 & 28.52 \\ Empirical (m/s) & 18.28 & 22.30 & 23.62 & 26.57 & 27.95 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Predicted average wind speeds as a function of the return period for Guangzhou. Figure 8: Three statistical models for comparing extreme wind speeds. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Probability** & **\(p\) Value** & **Test Statistic** & **Critical Value** (\%)** & **Critical Value** (\%)** \\ \hline [PERSON] & 0.0005 & 0.0676 & & \\ [PERSON] & 0.0624 & 0.0437 & 0.0454 & 0.0544 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: KS test results of extreme value distributions for Guangzhou. These observations suggest that the conservatism embedded in wind design codes may reflect a deliberate safety margin rather than unnecessary overdesign. Given the inherent uncertainties associated with tropical cyclone modeling, data limitations, and potential future climate change, such conservatism can be viewed as a necessary measure to ensure structural safety under rare or extreme conditions not fully represented in historical records. Nonetheless, in certain cases, it may also lead to conservative design outcomes, with implications for construction costs. Therefore, while the model results indicate that current code-specific wind speeds are higher than simulation-based predictions are, this conservatism likely serves as a practical safety buffer to account for unquantified risks.