Generally, climate variability has imposed formidable uncertainties and risks in productivity in tropics and subtropics. More specifically, seasonal rainfall variability has posed serious challenges to management practices that are merely dependent on climatological probabilities. Moreover, climate change has imposed unequivocal uncertainties and risks in productivity. As Ethiopia relies on natural rainfall for most of its water supply, exacerbated by existing marginal capabilities, it is very sensitive to seasonal rainfall variability. Therefore, using seasonal rainfall forecasts provides a promising opportunity to deal with such risks in advance and to improve sectorial resilience. However, in order to be usable for such applications, the rainfall forecasts need to provide better information than the climatologybased predictions. In technical expression, the seasonal rainfall forecasts must have some skill. In this study, skill of seasonal rainfall forecasts from European Centre for Medium-Range Weather Forecasts (ECMWF) Systems-4 was assessed over central-west Ethiopia using ‘WATCH Forcing Data methodology applied to ERA-Interim data’ (WFDEI) meteorological forcing data for validation. We used mainly one deterministic metrics and two probabilistic metrics to assess forecast skills. The area under the ROC curve for precipitation forecast below the lower tercile was computed to be 0.53, 0.54 and 0.6 at 0-, 1- and 2-months lead-times respectively. The area under the ROC curve for the upper tercile category was 0.54, 0.44 and 0.38 at 0-, 1- and 2-month lead-times respectively. Whereas, the Ranked Probability Skill Scores (RPSS) were computed to be -0.108, -0.1713 and -0.1226 at 0-, 1- and 2-month forecast leadtimes respectively. The predictive skill in ECMWF System-4 precipitation forecasts over the study area was generally poor, and nearly no benefit gained at any lead-time compared to the climatology.