Data quality is crucial for the reliability and accuracy of hydroclimatic studies. In data-scarce regions, time series are often incomplete, with many gaps and outliers. Moreover, changes in station location, instrument, or other conditions can cause shifts unrelated to natural variability, leading to false conclusions about climate trends. This study evaluates the quality and homogeneity of rainfall time series to fill in missing values and identify non-climatic inconsistencies. It focuses on the Upper Oum Er-Rbia basin in northern Morocco. To ensure the integrity of monthly rainfall data in six-gauge stations from 1970 to 2022, the Standard Normal Homogeneity Test (SNHT) method is employed in the R environment. As a result, quality control detected 12 outliers. The homogeneity test highlighted four breaks. The one observed in station S1 can be related to the construction of the Ahmed El-Hansali dam. The homogenized series consists of 79.3% of observed data, 6.3% of filled-in data, and 14.4% of corrected data. The RMSE calculated on the anomaly series shows low values with an average of 15.4 mm, indicating good performance of the SNHT test. The findings highlight the significance of employing rigorous statistical methods like SNHT to detect anomalies and ensure the reliability of climatic datasets.