Agricultural production is highly sensitive to climate and weather perturbations. Maize is the main crop cultivated in South Africa and production is predominantly rain-fed. South Africa’s climate, especially rainfall, is extremely variable which influences the water available for agriculture and makes rain-fed cropping very risky. In the aim to reduce the uncertainty in the climate of the forthcoming season, this study investigates whether seasonal climate forecasts can be used to predict maize yields for South Africa with a usable level of skill. Maize yield, under rain-fed conditions, is simulated for each of the magisterial districts in the primary maize producing region of South Africa for the period from 1979 to 1999. The ability of the CERES-Maize model to simulate South African maize yields is established by forcing the CERES-Maize model with observed weather data. The simulated maize yields obtained by forcing the CERES-Maize model with observed weather data set the target skill level for the simulation systems that incorporate Global Circulation Models (GCMs). Two GCMs produced the simulated fields for this study, they are the Conformal Cubic Atmospheric Model (CCAM) and the ECHAM4.5 model. CCAM ran a 5 and ECHAM4.5 a 6- member ensemble of simulations on horizontal grids of 2.1° x 2.1° and 2.8° x 2.8° respectively. Both models were forced with observed sea-surface temperatures for the period 1979 to 2003. The CERES-Maize model is forced with each ensemble member of the CCAM-simulated fields and with each ensemble member of the ECHAM4.5-simulated fields. The CERES-CCAM simulated maize yields and CERES-ECHAM4.5 simulated maize yields are combined to form a Multi-Model maize yield ensemble system. The simulated yields are verified against actual maize yields. The CERES-Maize model shows significant skill in simulating South Africa maize yields. CERES-Maize model simulations using the CCAM-simulated fields produced skill levels comparable to the target skill, while the CERES-ECHAM4.5 simulation system illustrated poor skill. The Multi-Model system presented here could therefore not outscore the skill of the best single-model simulation system (CERES-CCAM). Notwithstanding, the CERES-Maize model has the potential to be used in an operational environment to predict South African maize yields, provided that the GCM forecast fields used to force the model are adequately skilful. Such a yield prediction system does not currently exist in South Africa.