Enhancing the Value of Seasonal Forecasts through Post-Processing at AEMET: From Seasonal Systems to User-Oriented Information

In the context of climate change and the increasing frequency of unprecedented weather and climate events, seasonal forecasts are becoming an essential tool for adaptation, supporting decision-making and preparedness across strategic sectors and society at large. However, in mid-latitudes, seasonal prediction systems still exhibit limited skill and typically provide climate information averaged over the entire season, which may not offer the resolution and specificity required for some practical applications. This limitation is particularly relevant for extreme events, such as heavy precipitation, droughts, or heat waves, which can have substantial impacts on agriculture, hydrology, health, and energy sectors.

Post-processing techniques offer a promising pathway to enhance the relevance and applicability of seasonal forecasts. This work presents a set of post-processing approaches currently being developed and tested at AEMET, aimed at generating climate information better suited for decision-making. Statistical downscaling methods are applied to improve forecast skill by relating large-scale model outputs to observed local variations, including the effects of orography and local-scale climate features. Additionally, the availability of refined, higher-resolution information enables the development and evaluation of impact-based indices, including those focused on extreme precipitation events, droughts, and heat-stress indicators, tailored to support adaptation strategies across multiple sectors.

This work assesses the extent to which these post-processing strategies enhance the capability of seasonal forecast systems to provide skillful, reliable, and decision-relevant information. Special attention is given to the added value in terms of spatial detail, forecast reliability, and representation of climate-related risks, including high-impact events. The results highlight both the potential and limitations of current approaches, contributing to ongoing efforts to bridge the gap between seasonal climate prediction and its practical use in climate services.