Water scarcity is a major problem in arid and semi-arid areas. The scarcity of water is further stressed by the growing demand due to increase in population growth in developing countries. Climate change and its outcomes on precipitation and water resources is the other problem in these areas. Several models are widely used for modeling daily precipitation occurrence. In this study, Markov Chain Model has been extensively used to study spell distribution. For this purpose, a day period was considered as the optimum length of time. Given the assumption that the Markov chain model is the right model for daily precipitation occurrence, the choice of Markov model order was examined on a daily basis for 4 synoptic weather stations with different climates in Iran (Gorgan, Khorram Abad, Zahedan, Tabriz)during 1978-2009. Based on probability rules, events possibility of sequential dry and wet days, these data were analyzed by stochastic process and Markov Chain method. Then probability matrix was calculated by maximum likelihood method. The possibility continuing2-5days of dry and wet days were calculated. The results showed that the probability maximum of consecutive dry period and climatic probability of dry days has occurred in Zahedan. The probability of consecutive dry period has fluctuated from 73.3 to 100 percent. Climatic probability of occurrence of dry days would change in the range of 70.96 to 100 percent with the average probability of about 90.45 percent
