The scarcity of data has significantly hindered accurate precipitation analysis and forecasting in South Wollo. This study evaluates eight gridded precipitation datasets: Climate hazards infraRed precipitation with station data (CHIRPS), climatic research unit (CRU), European centre for medium-range weather forecasts reanalysis v5 (ERA5), global precipitation climatology centre (GPCC), global precipitation climatology project (GPCP), precipitation estimation from remotely sensed information using artificial neural networks-climate data record (PERSIANN-CDR), tropical applications of meteorology using SATellite and ground-based observations (TAMSAT) African rainfall climatology and time-series (TARCAT) (TAMSAT-TARCAT), and the coupled model intercomparison project phase 6 (CMIP6) ensemble mean using observed precipitation data from 1985 to 2014. Various statistical metrics were employed, including correlation coefficient, root mean squared error (RMSE), bias, Kling-Gupta efficiency (KGE), and trend analysis. Results showed that all datasets effectively captured the primary and secondary rainy seasons (July to September) and the secondary seasons (March to May). Seasonal correlations ranged from 0.60 (ERA5 during July to September) to 0.97 (CHIRPS during March to May). GPCC, GPCP, and CHIRPS were identified as the most reliable datasets for reproducing observed rainfall means. These findings enhance precipitation forecasting accuracy and inform agricultural planning and disaster management in this data-scarce region, crucial for adapting to climate variability and ensuring sustainable development in South Wollo.