Extreme value (EV) theory has raised researcher's attention for modelling and forecasting the catastrophic or high-risk events. Since extreme environmental events may cause huge loss of properties and affect human life, therefore it is significant to understand the behavior of such uncommon events and predict the upcoming. EV theory affords some understanding to the tail of a distribution where standard models have proved unreliable. The generalized extreme value (GEV) distribution is used to model the extreme based on block maxima method. Inference of the extremes of environmental events is essential as a guideline in designing structures to survive under the utmost extreme condition. Extreme temperature caused various effect and can be associated with human health and material damage. Meanwhile, El Nino Southern Oscillation (ENSO), is a climate condition that happened all year round globally. The EV theory is applied through GEV with two approaches, Maximum Likelihood Estimate (MLE) and L-Moments (LM) to model the extreme temperature in Chuping, Perlis and hence estimates the future extreme levels. The issue of block size is discussed. The goodness of fit (GOF) test is done using root mean squared error (RMSE) and Kolmogorov Smirnov (KS) test to determine the best approach. From the results, it is found that MLE is better than LM for the yearly block but LM squared things up for the monthly block. The return level is expected to exceed the maximum value 40.1oC at certain return periods. The moderate correlation coefficient, r is obtained in finding the relation between extreme temperature and ENSO. So, it is proofed that ENSO events influences extreme temperature in Chuping, Perlis.
