Statistical modelling of extreme temperature in Peninsular Malaysia

Abstract Extreme temperature events bring significant effects on the environment and society. Consequently, investigating best fit for extreme data is important hydrological study event forecasting. The main aim of this to determine probability distribution monthly annual temperatures. maximum at time scales were obtained from MMD (Malaysia Meteorological department). 40 years fitted 10 distributions each station. parameters estimated by likelihood method L-moment method. Besides, three goodness tests, namely Kolmogorov-Smirnov (K-S), Anderson-Darling (A2) Chi-Squared Error (CSE) test applied evaluate performances distributions. was selected based lowest scores summation tests. results showed that Generalized Value as best-fit distribution, followed Log-Pearson 3, 3 Parameter Lognormal, Log Logistic Gamma can be used a reference development planners, agricultural sector, water management agencies in planning disaster management.