## 6 Conclusions and Discussion  
This study serves as one of the very few fundamental works focusing on the century-long record of Hong Kong QBIE events. Historical Hong Kong QBIE heat waves and cold surges from 1885 to 2022 are identified based on station observations of daily mean, minimum, and maximum temperature. According to their occurrence time, all QBIE events were categorized according to their characteristics, namely, the combined daytime and nighttime (CDN), weak nighttime (WNT), strong nighttime (SNT), weak daytime (WDT), and strong daytime (SDT) QBIE events. The climatology, seasonality, diversity, long-term variability, and predictability of these QBIE events are examined. A total of 382 QBIE heat waves and 510 QBIE cold surges are identified from 1885 to 2022, exhibiting various characteristics in their occurring time and seasonality. Based on ARIMA model and time series analyses, we find that while apparent interannual variability exists in QBIE heat wave and cold surge activity, short-term climate prediction of QBIE temperature events based on past patterns or common climate indices is largely unfeasible. In general, there are more QBIE cold surges being observed in the past century than QBIE heat waves. The average intensity of QBIE cold surge is also overall stronger, indicating that the temperature anomalies on the quasi-biweekly to intraseasonal timescales show a rather skewed distribution, with more extreme values on the negative side. Among the different types of QBIE events, CDN events are the most commonly recorded in Hong Kong, accounting for more than 30% of all QBIE temperature events in Hong Kong. In addition, CDN events have the strongest average intensity compared to other types, followed by SNT and SDT events. The average durations of CDN, SNT, and SDT events are also longer than those of WNT and WDT events. Apparent seasonality is observed mostly on the occurrence frequency of QBIE events and the number of event days, but it shows large differences among different types of events. Given the importance and potential impacts of extreme weather in the QBWO and ISO timescales, we further examine how climate change has influenced historical QBIE  
Figure 9: Correlation coefficients of interannual variability in QBIE event counts with that in Arctic Oscillation (AO), North Atlantic Oscillation (NAO), El Niño–Southern Oscillation (ENSO, indicated by ONI and ONI_DJF), and Indian Ocean Dipole (IOD, indicated by DMI). Panels (**a,b**) present correlations with no time lags and 1-year time lag, respectively. On the y-axis, ONI and ONI_DJF denote the annual and boreal winter oceanic Niño index, respectively. DMI refers the Indian Ocean dipole mode index. On the x-axis, labels include prefixes that indicate the types of QBIE events and suffixes that denote heat waves (h) and cold surges (c). The black dots indicate correlation coefficients that are statistically significant at the 90% confidence level. Results are based on data from 1950 to 2022, the common period during which all climate indices are available. temperature events. Interestingly, our results show that there is a significant overall increase in QBIE cold surge events, although most statistics related to QBIE temperature events in Hong Kong do not exhibit significant long-term trends during the period of 1885-2022. This is, to some extent, inconsistent with previous findings that suggest climate change reduces the frequency of extreme cold surges [12]. However, it is noted that the timescales (10-60 days) of interest in this study differ from those in previous research, which mostly focuses on synoptic weather extremes (<7 days) [39; 40]. This discrepancy indicates that the response of extreme weather to climate change may be dependent on the timescales of these events [41]. More importantly, it highlights the need to develop strategies to mitigate the rising risks of long-lasting cold surge events in Hong Kong under future climate change.