Near-term climate predictions such as multi-year to decadal forecasts are increasingly being used to guide adaptation measures and building of resilience. To ensure the utility of multi-member probabilistic predictions, inherent systematic errors of the prediction system must be corrected or at least reduced. In this context, decadal climate predictions have further characteristic features, such as the long-term horizon, the lead-time-dependent systematic errors (drift) and the errors in the representation of long-term changes and variability. These features are compounded by small ensemble sizes to describe forecast uncertainty and a relatively short period for which typical pairs of hindcasts and observations are available to estimate calibration parameters. With DeFoReSt (Decadal Climate Forecast Recalibration Strategy), Pasternack et al. (2018) proposed a parametric post-processing approach to tackle these problems. The original approach of DeFoReSt assumes third-order polynomials in lead time to capture conditional and unconditional biases, second order for dispersion and first order for start time dependency. In this study, we propose not to restrict orders a priori but use a systematic model selection strategy to obtain model orders from the data based on non-homogeneous boosting. The introduced boosted recalibration estimates the coefficients of the statistical model, while the most relevant predictors are selected automatically by keeping the coefficients of the less important predictors to zero. Through toy model simulations with differently constructed systematic errors, we show the advantages of boosted recalibration over DeFoReSt. Finally, we apply boosted recalibration and DeFoReSt to decadal surface temperature forecasts from the German initiative Mittelfristige Klimaprognosen (MiKlip) prototype system. We show that boosted recalibration performs equally as well as DeFoReSt and yet offers a greater flexibility.
