## 2 Materials and Methods  
Kuching City, shown in Figure 1, experiences an average annual rainfall of 4096 mm, with the highest monthly distribution reaching 485.4 mm [16]. Daytime temperatures in Kuching range from 23 to 32 \({}^{\circ}\)C [4]. The weather in Kuching is influenced by various factors, including the Madden-Julian Oscillation (MJO), El Nino-Southern Oscillation (ENSO), monsoons, and the Indian Ocean Dipole (IOD) [5]. The northeast monsoon season occurs from November to February, while the northwest monsoon occurs from May to September, resulting in relatively dry weather during this period [5]. The monsoon transition occurs in April and October. Kuching City was selected as the study location due to the significant impact of El Nino events in 1982-1983 and 1997-1998, which led to a 25-28% reduction in rainfall and prolonged droughts. [PERSON] [17] also reported a 40% decrease in rainfall from May to October 1997 compared to the normal monthly average. Kuching City was specifically chosen as the study location to assess the influence of ENSO on temperature for several compelling reasons. Firstly, Kuching City is situated in a tropical climate zone characterized by consistently high temperatures and humidity throughout the year. Such regions are highly susceptible to the impacts of climate phenomena such as ENSO, which can have a significant effect on temperature patterns and variability. Moreover, Kuching City, like many other tropical cities, faces unique vulnerabilities related to temperature changes, including high population density, the urban heat island effect, and limited access to cooling infrastructure [6]. Furthermore, Kuching City has experienced rapid urban growth compared to other cities in East Malaysia, making it crucial to investigate the specific effects of ENSO on temperature in this context. This study aims to provide valuable insights into the city's vulnerability to heat during ENSO events. Our findings will inform future preparations and contribute to building a more resilient city capable of coping with the potential impacts of climate change. By examining the impact of ENSO on the temperature in Kuching City, researchers can gain a deeper understanding of the local climate dynamics, identify temperature trends  
Figure 1: Location of study (Google Earth). during different ENSO phases, and contribute to climate change adaptation strategies for tropical urban areas. This research utilized Landsat data from Landsat 5 TM, 7 ETM, and 8 TIR OLI sensors. The selection of remote sensing (RS) data from the period 1988-2019 was limited to cloud-free cover in the study area. A total of 37 Landsat images were downloaded from the USGS Earth Explorer [18]. The MODIS-coded MOD11C3 dataset provided average monthly surface temperature data at a resolution of 5600 m per pixel. The temperature measurements were captured by the MODIS satellite, specifically the Terra sensor, which has been operational since 1999. The study utilized 239 MODIS data files from the years 2000 to 2019, which were obtained from the NASA LADS Web website [19]. The choice of using the MOD11C3 product was based on an examination of data quality by the MODIS team. These processed data are widely utilized in surface temperature studies during both daytime and night-time [6; 18]. The surface temperature is derived and generated using the general split-window algorithm, with the MOD11C3 product specifically employing bands 31 and 32, which are thermal bands designed for measuring surface temperature on Earth [6; 20]. The Oceanic Nino Index (ONI) is a crucial tool used to identify and characterize El Nino and La Nina events. It is derived from sea surface temperature anomalies, providing insights into the development and intensity of these phenomena in the Pacific Ocean. The ONI calculation focuses on a specific region known as Nino zone 3.4, which extends from 5\({}^{\circ}\) north to 5\({}^{\circ}\) south and 120\({}^{\circ}\) to 170\({}^{\circ}\) west. Table 1 describes the scale intensity level based on the ONI.