| Summary: | The main objective of this study is to examine trend and homogeneity through the analysis of rainfall variability patterns in Iran. The study presents a review on the application of homogeneity and seasonal time series analysis methods for forecasting rainfall variations. Trend and homogeneity methods are applied in the time series analysis from collecting rainfall data to evaluating results in climate studies. For the homogeneity analysis of monthly, seasonal and annual rainfall, homogeneity tests were used in 140 stations in the 1975–2014 period. The homogeneity of the monthly and annual rainfall at each station was studied using the autocorrelation (ACF), and the von Neumann (VN) tests at a significance level of 0.05. In addition, the nature of the monthly and seasonal rainfall series in Iran was studied using the Kruskal-Wallis (KW) test, the Thumb test (TT), and the least squares regression (LSR) test at a significance level of 0.05. The present results indicate that the seasonal patterns of rainfall exhibit considerable diversity across Iran. Rainfall seasonality is generally less spatially coherent than temporal patterns in Iran. The seasonal variations of rainfall decreased significantly throughout eastern and central Iran, but they increased in the west and north of Iran during the studied interval. The present study comparisons among variations of patterns with the seasonal rainfall series reveal that the variability of rainfall can be predicted by the non-trended and trended patterns.
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