Downscaling is a quantitative way of relating the large-scale climatic predictor variables to the local scale meteorological variables to overcome the ineffectiveness of the GCM model output. However, despite the high relevance and sophistication of this new method in climatological studies, the results are not completely free of uncertainties. The aim of the study was to assess the level of uncertainties associated with rainfall projection using statistical downscaling techniques under climate change scenarios over the south-south region, Nigeria. The expost-facto research design was adopted for the study while the quadrat sampling technique was used to determine the sample size by stratifying the area into 2° x 2° latitude and longitude intersections and each weather station that fall within the grids (Asaba, Warri, Uyo and Port Harcourt) was calibrated and selected for the study. Data used for this study were mainly secondary data and it includes 30 years rainfall data (1985-2015) which was acquired from the archives of Nigerian Meteorological Agency (NiMet) and large-scale predictors assessed from the archives of the National Centre for Environmental Prediction (NCEP). The Multiple Regression Analysis was used in the selection of large-scale predictors with strong relationship with the predictand. Consequently, shum, rhum, r850, r500, p5_u, p_u, & p5th were selected as the principal large-scale predictors of rainfall in the area. On the other hand, Wilcoxon signed rank test was employed to perform the uncertainty analysis and the results shows uncertainty associated with rainfall projections in the area at P<0.05 in some of the months. The validation process reveals R and RMSE ranging between, R (0.64-0.91) and RMSE (0.11-0.43) indicating a better performance of the model on seasonal timescale particularly in Asaba at DJF, Warri in JJA while Port Harcourt and Uyo in SON. Based on the findings of the study, development of a local climate management system in preparedness for climate change, climate change planning and policy formuations and committed efforts to maintain B2 scenario with reduced GHG‟s emission were recommended.