Seasonal weather forecasts are crucial for long-term planning in many practical situations and skillful forecasts may have substantial economic and humanitarian implications. Current seasonal forecasting models require statistical postprocessing of the output to correct systematic biases and unrealistic uncertainty assessments. A particular challenge for seasonal weather forecasts is that the available time series for training are typically short, calling for robust postprocessing techniques. Moreover, the prediction error exhibits spatial correlation patterns that are often non-stationary and non-isotropic. We propose a multivariate postprocessing approach utilizing covariance tapering, combined with a dimension reduction step based on principal component analysis for efficient computation. In an application to global sea surface temperature forecasts issued by the Norwegian Climate Prediction Model (NorCPM), our proposed methodology is shown to outperform known reference methods.