This study provides a comprehensive evaluation of eight high-spatial-resolution gridded precipitation products in semi-Arid regions of Tamil Nadu, India, focusing specifically on Coimbatore, Madurai, Tiruchirappalli, and Tuticorin, where both irrigated and rainfed agriculture is prevalent. The study regions lack sufficiently long-Term and spatially representative observed precipitation data, which are essential for agro-hydrological studies and better understanding and managing the nexus between food production and water and soil management. Hence, the present study evaluates the accuracy of five remote-sensing-based precipitation products, namely the Tropical Rainfall Measuring Mission (TRMM), the Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Climate Data Record (PERSIANN CDR), the CPC MORPHing technique (CMORPH), the Integrated Multi-Satellite Retrievals for Global Precipitation Measurement (GPM-IMERG), and the Multi-Source Weighted-Ensemble Precipitation (MSWEP), and three reanalysis-based precipitation products, namely the National Centers for Environmental Prediction Reanalysis 2 (NCEP2), the European Centre for Medium-Range Weather Forecasts (ECMWF) Reanalysis version 5 Land (ERA5-Land), and the Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA2), against the station data. Linearly interpolated precipitation products were statistically evaluated at two spatial (grid and district-wise) and three temporal (daily, monthly, and yearly) resolutions for the period 2003-2014. Based on overall statistical metrics, ERA5-Land was the best-performing precipitation product in Coimbatore, Madurai, and Tiruchirappalli, with MSWEP following closely behind. In Tuticorin, however, MSWEP outperformed the others. On the other hand, MERRA2 and NCEP2 performed the worst in all the study regions, as indicated by their higher root mean square error (RMSE) and lower correlation values. Except in Coimbatore, most of the precipitation products underestimated the monthly monsoon precipitation, which highlights the need for a better algorithm for capturing convective precipitation events. Moreover, the percent mean absolute error (%MAE) was higher in non-monsoon months, indicating that product-based agro-hydrological modelling, like irrigation scheduling for water-scarce periods, may be less reliable. The ability of the precipitation products to capture extreme-precipitation intensity differed from the overall statistical metrics, where MSWEP performed the best in Coimbatore and Madurai, PERSIANN CDR in Tiruchirappalli, and ERA5-Land in Tuticorin. This study offers crucial guidance for managing water resources in agricultural areas, especially in regions with scarce precipitation data, by helping to select suitable precipitation products and bias correction methods for agro-hydrological research.