Enhancing seasonal forecasting in mountain regions of the Iberian Peninsula through statistical downscaling

Mountainous areas are of particular interest due to their critical role in the hydrological resource, especially in the Iberian Peninsula, a region subjected to strong climate variability and change. In this work, we seek to evaluate the seasonal prediction of precipitation and temperature over these areas. Interannual variability of these two variables is of great importance, as it influences the onset, offset, melting rate and other characteristics of the seasonal snow cover. The seasonal snowpack is of great relevance, as its melting during late spring can reduce the impact of the summer drought by providing a crucial water resource.

To date, seasonal prediction models lack the necessary spatial resolution to resolve processes regarding complex orography or local phenomena. Furthermore, even widely used reanalysis products cannot accurately represent alpine sites, and therefore high-resolution products or on-site observations are required. In this regard, statistical downscaling methods can provide a necessary improvement at low computational costs. In this study, statistical methods, such as the analogs method and Principal Component Analysis (PCA), are employed to link large-scale atmospheric patterns predicted by seasonal prediction systems, such as ECMWF's fifth generation seasonal forecast system (SEAS5), to local data in the form of observations or high-resolution gridded products. These methods can provide a more accurate representation of local climatology in mountain regions, thereby improving seasonal forecasting skill in such crucial areas. Furthermore, the methodology allows for the calculation of snow-related indices that may prove useful for early water management decisions and for the interests of the ski industry. The findings of this research have significant implications for seasonal prediction and climate services, contributing to our understanding of climate variability and predictability in the Iberian Peninsula and in high-elevation areas within.