Assessing the performance of global precipitation products in semi-arid Mediterranean catchments is crucial for improving hydrological modeling and water resource management, particularly in regions where in-situ observations are scarce or nonexistent. This study evaluates five precipitation datasets: observed rainfall, CHIRPS (Climate Hazards Group InfraRed Precipitation with Station data), PERSIANN (Remotely Sensed Information using Artificial Neural Networks), GPM-IMERG (Global Precipitation Measurement Integrated Multi-Satellite Retrievals), and ERA5 over a 17-year period from 2001 to 2017, including one year of model warm-up for a semi-arid catchment in central Tunisia. The Soil and Water Assessment Tool (SWAT), a semi-distributed hydrological model, was calibrated using observed precipitation data and then applied with alternative datasets to quantify the influence of precipitation sources on model performance. Calibration with observed data yielded satisfactory results, with a Nash-Sutcliffe Efficiency (NSE) of 0.63 and a Kling-Gupta Efficiency (KGE) of 0.72. When alternative datasets were introduced, CHIRPS consistently provided acceptable results even without recalibration, achieving a KGE value of 0.32, which indicates moderate model performance although it remains below the commonly accepted satisfactory threshold. ERA5 and PERSIANN showed slight improvement after calibration but often yielded low KGE values. During calibration, CHIRPS and ERA5 also achieved satisfactory performance, with NSE values of 0.51 and 0.50 and KGE values of 0.58 and 0.61, respectively. In contrast, GPM-IMERG results remained largely unchanged, showing minimal sensitivity to calibration parameters. These findings confirm that precipitation is the principal source of uncertainty in hydrological modeling and that each dataset imposes specific ranges on calibrated parameters. For the Haffouz catchment, CHIRPS demonstrated the best spatial distribution and overall hydrological performance, highlighting the value of satellite-based precipitation products as reliable alternatives in data-scarce regions. This study provides critical insights, emphasizing the importance of carefully selecting precipitation datasets to reduce uncertainty and improve the reliability of hydrological simulations in semi-arid Mediterranean context. [GRAPHICS]