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Validation of Seasonal Forecasts: Statistical Methods and Downscaling

In this talk we present a simple statistical method to validate seasonal forecasts, comparing them with random predictions. This method provides an estimation of the statistical significance of the skill and, hence, allows us to find out predictable situations where the seasonal system significantly outperforms a random forecast. We also analyze the advantages of post-processing the predictions with some appropriate statistical downscaling method. The method is applied to precipitation and temperature forecasts considering two regions with different seasonal behavior: Peru (in the tropics) and Spain (mid-latitudes). Results show high predictability over Peru during El Nino periods. Here, the use of a downscaling method clearly improves the forecast skill. Over Spain the forecast signal is much weaker, but some predictability related to El Nino and La Nina events is found.

Finally, some sensitivity studies are presented. On the one hand, we compare raw station data with high resolution gridded interpolated data. On the other hand, different temporal aggregation patterns are used for the analog downscaling method (daily, weekly and monthly), comparing the obtained results.