Statistical downscaling applied to the CMCC Seasonal Prediction System 3.5

The Italian Peninsula's climate is highly influenced by its complex topography and diverse regional weather systems, making high-resolution (HiRes) seasonal forecasting crucial for agriculture, water management, and energy sectors. Traditional seasonal prediction models, such as the CMCC Seasonal Prediction System (SPS3.5), provide valuable insights but lack the spatial resolution necessary to capture local-scale climatic details. Recent advances in Statistical Downscaling (SD) promise enhancing these coarse-resolution forecasts by generating more localised and accurate predictions. Thus, this study aims to provide a HiRes seasonal forecast for the Italian Peninsula by enhancing the SPS3.5 model through SD techniques tailored to the region's demand for finer-scale climate information.
The downscaling method involves a three-step process that utilises historical observational datasets and machine-learning techniques to refine SPS3.5 forecasts. The first regards the ground truth, composed of HiRes observational data from ERA5 reanalysis for 2m temperature (T2m), sea surface temperature, and 10m wind components, and from CHIRPS for precipitation. Then, SPS3.5 daily forecasts are spatially interpolated from 1º to 1/4° to match the observation data's grid. Finally, both data are combined through a machine-learning method based on the k-Nearest Neighbours (kNN) technique, which translates SPS3.5 into HiRes fields by matching forecasted conditions to observed patterns. The kNN algorithm utilises a set of k days of similar weather conditions (five predictors mentioned before) determined by the Euclidean distance to capture seasonally relevant weather analogues. Once the analogue days are defined, the kNN can forecast any meteorological field within the observational dataset. Finally, the SD method was accessed over the Italian Peninsula domain through cross-validation along the 24-year hindcast period available for SPS3.5 (1993-2016).
Preliminary results indicate that SD significantly enhances seasonal forecasts for the Italian Peninsula, achieving biases about 5-6 times smaller than the original SPS3.5 for all evaluated predictands. The main component of this improvement is the spatial accuracy promoted by downscaling, allowing the identification of domain characteristics unnoticed in SPS3.5. Even though the statistical indices show appreciable values for the domain as a whole when we evaluate smaller portions of this same domain, the original seasonal forecasts are still far from the desired. As expected, forecast bias increases with lead time also for kNN, with accuracy declining progressively from lead month 1 onward. For example, T2m bias increased from -0.14/-0.85°C in lead month 1 to -0.68/-1.41°C in month 6 (kNN/SPS3.5). This trend highlights the ongoing challenge of maintaining forecast skills over extended periods and the importance of adaptive correction strategies to extend lead-time reliability.
Integrating SD techniques with SPS3.5 outputs provides a promising solution for generating HiRes seasonal forecasts, offering valuable support for climate-sensitive applications by reducing forecast bias and enhancing spatial accuracy. This work demonstrates the potential of SD as an effective tool for bridging the gap between coarse seasonal forecasts and the localised weather information necessary for effective decision-making.