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Statistical downscaling methods are popular post-processing tools which are widely used in many sectors to adapt the coarse-resolution biased outputs from global climate simulations to the regional-to-local scale typically required by users. They range from simple and pragmatic Bias Correction (BC) methods, which directly adjust the model outputs of interest (e.g. precipitation) according to the available local observations, to more complex Perfect Prognosis (PP) ones, which indirectly derive local predictions (e.g. precipitation) from appropriate upper-air large-scale model variables (predictors). Statistical downscaling methods have been extensively used and critically assessed in climate change applications; however, their advantages and limitations in seasonal forecasting are not well understood yet. In particular, a key problem in this context is whether they serve to improve the forecast quality/skill of raw model outputs beyond the adjustment of their systematic biases. In this paper we analyze this issue by applying two state-of-the-art BC and two PP methods to downscale precipitation from a multimodel seasonal hindcast in a challenging tropical region, the Philippines. To properly assess the potential added value beyond the reduction of model biases, we consider two validation scores which are not sensitive to changes in the mean (correlation and reliability categories). Our results show that, whereas BC methods maintain or worsen the skill of the raw model forecasts, PP methods can yield significant skill improvement (worsening) in cases for which the large-scale predictor variables considered are better (worse) predicted by the model than precipitation. For instance, PP methods are found to increase (decrease) model reliability in nearly 40% of the stations considered in boreal summer (autumn). Therefore, the choice of a convenient downscaling approach (either BC or PP) depends on the region and the season., This study was partially supported by the SPECS and EUPORIAS projects, funded by the European Commission through the Seventh Framework Programme for Research under grant agreements 308378 and 308291, respectively. JMG acknowledges partial support from the project MULTI-SDM (CGL2015-66583-R, MINECO/FEDER).

Forecasting seasonal variations in European summer weather represents a considerable challenge. Here, we assess the performance of a seasonal forecasting model at representing a major mode of northern hemisphere summer climate variability, the circumglobal teleconnection (CGT), and the implications of errors in its representation on sea7 sonal forecasts for the European summer (June, July, August). Using seasonal hindcasts initialised at the start of May, we find that the model skill for forecasting the interannual variability of 500 hPa geopotential height is poor, particularly over Europe and several other “centres of action” of the CGT. The model also has a weaker CGT pattern than is observed, particularly in August, when the observed CGT wavetrain is strongest. We investigate several potential causes of this poor skill. First, model variance in geopotential height in west-central Asia (an important region for the maintenance of the CGT) is lower than observed in July and August, associated with a poor representation of the link between this region and Indian monsoon precipitation. Second, analysis of the Rossby wave source shows that the source associated with monsoon heating is both too strong and displaced to the northeast in the model. This is related to errors in monsoon precipitation over the Bay of Bengal and Arabian Sea, where the model has more precipitation than is observed. Third the model jet is systematically shifted northwards by several degrees latitude over large parts of the northern hemisphere, which may affect the propagation characteristics of Rossby waves in the model.

Recurrent climate winter regimes are examined from statistically reconstructed and modelled 500 hPa geopotential height fields over the North Atlantic/European sector for the period 1659–1990. We investigate the probability density function of the state space spanned by the first two empirical orthogonal functions of combined winter data. Regimes are detected as patterns that correspond to areas of the state space with an unexpected high recurrence probability using a Monte Carlo approach. The reconstruction and the model reveal four recurrent climate regimes. They correspond to the two phases of the North Atlantic Oscillation and two opposite blocking patterns. Complemented by the investigation of the temporal evolution of the climate regimes this leads to the conclusion that the reconstructed and the modelled data for this geographic sector reproduce low-frequency atmospheric variability in the form of regime-like behaviour. The overall evidence for recurrent climate regimes is higher for the model than for the reconstruction. However, comparisons with independent data sources for the period 1659–1990 revealed a more realistic temporal evolution of the regimes for the reconstructed data.