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A stochastic model based on first-order Markov chain was developed to simulate daily rainfall. The model is capable of simulating daily rainfall data of any length, based on the estimated transitional probabilities, mean, standard deviation and skew coefficients of rainfall amounts. A study of rainfall probability is an approach to sound planning for any variation of rainfall either small or large. The simulation model has been used successfully to estimate daily rainfall. The Multivariate logistic regression is used to estimate the probability that it is raining. The logistic regression technique is used to compare between the actual and simulation results for a rainfall from January to December in Bangladesh. The probability of occurrence of rainfall is of vital importance in efficient planning and execution of water use program. This study describes a crop-climate simulation model under rainfed conditions in Bangladesh to be used as a tool for analyzing growth and yield to help planning and management of rice production.