Global hydrological models can fill crucial gaps for providing essential information on water resources management, flood and drought forecasting, and assessing the impacts of climate change. However, these models face several challenges that must be addressed to ensure their applicability at local scales. These challenges include effectively managing Big Data, proper communication, adoption, and achieving accuracy in their results. Achieving accuracy in global hydrological models is critical for acceptance in decision-making, but poses the most significant challenge due to the extensive amount of observed data required and the complexity of obtaining and preparing such data for model evaluation. In this study, I conducted an evaluation of the GEOGloWS ECMWF Streamflow Services (GESS) historical simulation and forecast. The evaluation revealed the presence of systematic biases inherent in global models, which restrict their accuracy and reliability for local applications. To address this limitation, I propose a bias correction methodology that uses local data and employs a quantile-mapping approach to correct the systematic biases in the GESS model. I applied this methodology to the +40 years historical simulation dataset and forecast files released between January 1, 2014, and December 31, 2019, demonstrating its effectiveness in correcting the magnitude and seasonality of simulated streamflow values. Additionally, to enhance communication and adoption of the GESS model, I developed a web application called Historical Validation Tool (HVT) that processes and visualizes observed and simulated historical stream discharge data from the GESS model, performs bias correction on the historical simulation, computes goodness-of-fit metrics, and applies forward bias correction to subsequent forecasts. This web application was customized specifically for Brazil, Colombia, Ecuador, and Peru within the framework of the NASA SERVIR Amazonia Project. HVT enables users from these countries to get adjusted GESS historical simulations and forecasts, enhancing the reliability of GESS modeling results at the local scale. The results demonstrate that the bias correction method significantly improves the accuracy of the GESS historical simulation and forecast, as evidenced by the Kling Gupta Efficiency, making it a valuable tool for hydrological studies and water resources management. Furthermore, HVT with its user-friendly graphical interface, rapid performance, and flood alert capabilities, effectively communicates the improvements in GESS historical and forecasted data.