Estimating long-lead time precipitation under the stress of increased climatic variability is a challenging task in the field of hydrology. A modified Support Vector Machine (SVM) based framework is proposed to estimate annual precipitation using oceanic-atmospheric oscillations. Oceanic-atmospheric oscillations, consisting of Pacific Decadal Oscillation (PDO), North Atlantic Oscillation (NAO), Atlantic Multidecadal Oscillation (AMO), and El Niño–Southern Oscillation (ENSO) for a period of 1900–2008, are used to generate annual precipitation estimates with a 1 year lead time. The SVM model is applied to 17 climate divisions encompassing the Colorado River Basin in the western United States. The overall results revealed that the annual precipitation in the Colorado River Basin is significantly influenced by oceanic-atmospheric oscillations. The long-term precipitation predictions for the Upper Colorado River Basin can be successfully obtained using a combination of PDO, NAO, and AMO indices, whereas coupling AMO and ENSO results in improved precipitation predictions for the Lower Colorado River Basin. The results also show that the SVM model provides better precipitation estimates compared to the Artificial Neural Network and Multivariate Linear Regression models. The annual precipitation estimates obtained using the modified SVM modeling framework may assist water managers in statistically understanding the hydrologic response in relation to large scale climate patterns within the Colorado River Basin.