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Non-climatic reasons, such as station replacement and changing the measurement device and calculation method, may make climate data unrepresentative of the actual variation of the regional climate. Data quality control and homogenization tests for climate data are critical. Thus, this study aims to evaluate the quality and homogeneity level of precipitation time series in arid and semi-arid climatic zones and specify the breakpoint in the datasets. The proposed methodology has been used to conduct arid and semi-arid representative case studies comprising 40 annual precipitation series for Iraq between 1979 and 2014. A Box-Cox transformation has been used to adjust the non-normally distributed datasets. Outliers have been censored by truncating extreme values. The results of the outliers indicate that they may be caused more by climate variability than by human-induced reasons. Homogeneity adjustments have been developed by applying these four homogeneity tests: Pettitt’s test, the Standard Normal Homogeneity method, Buishand’s test, and von Neumann’s check. Approximately 40% of the series (i.e., 16 stations out of 40) were homogeneous. Each homogeneity test was evaluated separately, and non-homogeneous stations were identified. Then, the series was classified into three groups that were assigned the labels “useful”, “doubtful”, and “suspect”. The results indicated that twenty-one stations were associated with the class ‘suspect’, three of the stations belonged to the class ‘doubtful’, and sixteen locations were within the class ‘useful’. Furthermore, the data analysis indicated no influence of the outliers on the results of the homogeneity tests. Accordingly, the study recommends further research on homogeneity tests that can be applied without considering outlier tests for similar case studies.