Data from the regulated 14,000 km2 upper Des Moines River basin and a coupled forecast-control model are used to study the sensitivity of flow forecasts and reservoir management to climatic variability over scales ranging from daily to interdecadal. Robust coupled forecast-control methodologies are employed to minimize reservoir system sensitivity to climate variability and change. Large-scale hydrologic-hydraulic prediction models, models for forecast uncertainty, and models for reservoir control are the building blocks of the methodology. The case study concerns the 833.8¿106 m3 Saylorville reservoir on the upper Des Moines River. The reservoir is operated by the U.S. Corps of Engineers for flood control, low-flow augmentation, and water supply. The total record of 64 years of daily data is divided into three periods, each with distinct characteristics of atmospheric forcing. For each climatic period the coupled forecast-control methodology is simulated with a maximum forecast lead time of 4 months and daily resolution. For comparison, the results of operation using current reservoir control practices were obtained for the historical periods of study. Large differences are found to exist between the probabilistic long-term predictions of the forecast component when using warm or cool and wet or dry initial conditions in the spring and late summer. Using ensemble input corresponding to warm or cool and wet or dry years increases these differences. Current reservoir management practices cannot accommodate historical climate variability. Substantial gain in resilience to climate variability is shown to result when the reservoir is operated by a control scheme which uses reliable forecasts and accounts for their uncertainty. This study shows that such coupled forecast-control decision systems can mitigate adverse effects of climatic forcing on regional water resources. ¿ 1998 American Geophysical Union