  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. We propose a multivariate postprocessing
approach utilizing covariance tapering, combined with a dimension reduction
step based on principal component analysis for efficient computation. Our
proposed technique can correctly and efficiently handle non-stationary,
non-isotropic and negatively correlated spatial error patterns, and is
applicable on a global scale. Further, a moving average approach to marginal
postprocessing is shown to flexibly handle trends in biases caused by global
warming, and short training periods. 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.
