Missing data is a common problem in all scientific research and the availability of gap-free data is rare in most developing countries. Statistical and empirical methods are the most often used for approximation of missing data. The performance of eight missing estimation methods was evaluated using bias, RMSE, and NSE. The Multicriteria decision method of compromise programing approach was used to identify the best imputation method. Four homogeneity test methods were used to evaluate the

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... eneity of the time series data. The Mann-Kendall trend test and Son's slope were used to locate the change and calculate the magnitude of the trend. Multiple Linear Regression and Multiple Imputation by chained equations were well performed over most stations. Alternatively, the modified version of IDWM based on spatial distance and elevation difference (IDWME&D3) was ranked as third at many stations. The inclusion of elevation differences between stations has improved the capability of the inverse distance weight method. However, the performance of all the missing estimation methods decreased as the percentage of missing increased. Radius influences have no significant impact on the performance of missing imputation methods. Only Butajera station exhibited nonhomogeneous. Dagaga, Iteya, Bui, and Butajera stations all exhibited decreasing trends. Kulumsa and Ejerese-lele stations presented an increasing trend in monthly rainfall. However, the rest station has shown no significant increasing or decreasing trends in monthly rainfall.