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The current study improves streamflow forecast lead-time by coupling climate information in a data-driven modeling framework. The spatial-temporal correlation between streamflow and oceanic-atmospheric variability represented by sea surface temperature (SST), 500-mbar geopotential height (Z(500)), 500-mbar specific humidity (SH500), and 500-mbar east-west wind (U-500) of the Pacific and the Atlantic Ocean is obtained through singular value decomposition (SVD). SVD significant regions are weighted using a nonparametric method and utilized as input in a support vector machine (SVM) framework. The Upper Rio Grande River Basin (URGRB) is selected to test the applicability of the proposed model for the period of 1965-2014. The April-August streamflow volume is forecasted using previous year climate variability, creating a lagged relationship of 1-13 months. SVD results showed the streamflow variability was better explained by SST and U-500 as compared to Z(500) and SH500. The SVM model showed satisfactory forecasting ability with best results achieved using a one-month lead to forecast the following four-month period. Overall, the SVM results showed excellent predictive ability with average correlation coefficient of 0.89 and Nash-Sutcliffe efficiency of 0.79. This study contributes toward identifying new SVD significant regions and improving streamflow forecast lead-time of the URGRB.