## 2 Materials and Methods
### Methods
#### 2.3.2 Heatwave Definition and Its Metric Detection  
To date, a universally accepted definition of heatwaves remains inconsistent. Previous investigations have adopted a variety of methodologies, leading to variations influenced by meteorological conditions, socio-demographic attributes, acclimatization processes, and geographic factors [94]. Nonetheless, a common metric employed by numerous investigators involves the 90 th percentile of daily maximum and minimum temperatures, designated as CTX90 ct and CTN90 ct, respectively. This methodology has been adopted in various studies [95; 96; 97], establishing a standardized and globally relevant framework for quantifying heatwave characteristics such as frequency, duration, and intensity. Such a standardized approach facilitates the derivation of threshold values that are instrumental across a multitude of geographical locales and sectors of impact [38; 98], proving particularly valuable in tropical regions, including Thailand [99].  
In this study, the detection and measurement of \(T_{\text{air}}\) and LST heatwaves are conducted using established methods referenced in [95; 100; 101] and summarized in Table 2. A heatwave is defined as a period when temperatures exceed a certain threshold for at least three consecutive days, effectively identifying sustained extreme temperature events and reflecting temporal variations. This methodology, supported by findings from [30], is particularly well suited for Southeast Asia due to its complex land-sea configuration and diverse topographies, offering a nuanced approach to detecting regional heatwave patterns. We observe both daytime and nighttime heatwaves as widely used in [102; 103; 104; 105; 106]. Daytime heatwaves, essentially defined by high daytime maximum temperatures (\(T_{\text{max}}\)), are accompanied by increased downward shortwave radiation under clear skies with re duced cloud cover and moisture, as well as lower humidity. These hot and dry daytime conditions can lead to potential impacts such as wildfires, water deficits, reduced crop yields, and increased human health risks [107]. In contrast, nighttime heatwaves, often measured by high nightly minimum temperatures (\(T_{\text{min}}\)), typically occur under moist conditions characterized by increased cloud fraction, humidity, and long-wave radiation at the surface. These conditions significantly affect human comfort and inhibit recovery from the heat experienced during the daytime, thus increasing the threat to human health from high-temperature weather. More hazardous conditions emerge when extreme daytime temperatures are combined with warm nighttime conditions for consecutive days, creating compound heatwaves [106].  
To define daytime heatwave events, this study uses daily maximum air temperatures (\(T_{\text{max}}\)) from MODIS-MOD11A1 day and MODIS-MYD11A1 day datasets. Nighttime events are similarly identified using daily minimum air temperatures (\(T_{\text{min}}\)) from MODIS-MOD11A1 night and MYD11A1 night. Heatwaves are determined using a criterion based on the 90 th percentile of the calendar day temperatures.  
This threshold captures the annual variation in extreme heat, with a unique percentile calculated for each day of the study period. As a result, this approach encompasses all heatwave events occurring from the start to the end of the period of interest. The CTX90 ct method is utilized for \(T_{\text{max}}\) and LST day (MOD11A1 day, MYD11A1 day), while the CTN90 pt method is employed for \(T_{\text{min}}\) and LST night (MOD11A1 night, MYD11A1 night), the 90 th percentile threshold (\(T_{90}\)) for a given set of temperature data \(T\) is calculated as  
\[T_{90}=\text{Percentile}(T,90), \tag{7}\]  
where \(\text{Percentile}(T,90)\) represents the temperature value below which 90% of the observations fall in the dataset (\(T\)).