Rising temperature in the atmosphere causes sea level rise and affects low lying coastal areas and deltas of the world. The last decade of the twentieth century was globally the hottest since the beginning of worldwide temperature measurement during the nineteenth century. Many PDFs have been proposed in recent past, but in present study Weibull, Lognormal, Gamma, GEV, etc are used to describe the characteristics of maximum temperature. This paper attempts to determine the best fitted probability distribution of monthly maximum temperature. To identify the appropriate probability distribution of the observed data, this paper considers a data set on the monthly maximum temperature of two coastal stations (Cox’s Bazar and Patuakhali) over the period January, 1971 to November, 2015 and January, 1973 to November, 2015 respectively. To check the accuracy of the predicted data using theoretical probability distributions the goodness-of-fit criteria like KS, R², χ2, and RMSE were used in this paper. According to the goodness-of-fit criteria and from the graphical comparisons it can be said that Generalized Skew Logistic distribution (GSL) provides the best fit for the observed monthly maximum temperature data of Cox’s Bazar and Weibull (W) gives the best fit for Patuakhali among the probability distributions considered in this paper.