Exploring Post-Processing for Seasonal Forecasts of Extremes in the Subtropics

Predicting extreme climate events at seasonal timescales remains a significant scientific and operational challenge, particularly in subtropical regions, where variability is strongly influenced by large-scale circulation patterns and where impacts on water resources, ecosystems, and human health are especially critical. These regions are often characterized by pronounced climate variability and exposure to high-impact events, making the reliable prediction of extremes a key priority. While dynamical forecasting systems generally exhibit skill in representing large-scale modes of variability, they often struggle to accurately translate these signals into consistent and reliable regional-scale responses. This mismatch, combined with a low signal-to-noise ratio, limits the direct usability of raw model outputs for decision-making.

This work presents an exploratory assessment of post-processing strategies aimed at enhancing the representation and predictability of temperature and precipitation extremes in subtropical areas. Using ensemble outputs from dynamical seasonal prediction systems, we investigate the extent to which statistical post-processing can improve the characterization of extremes, defined using percentile-based indices. The analysis adopts as a flexible and data-driven framework, focusing on identifying relationships between large-scale predictors and regional-scale extreme responses, as well as on improving the consistency and robustness of probabilistic forecasts.

Preliminary results suggest that even relatively simple post-processing approaches can add value to the prediction of seasonal extremes, particularly by better exploiting the large-scale information already captured by dynamical models. These findings highlight the potential of post-processing as a complementary tool for improving forecast usability and supporting climate services and risk-informed decision-making in vulnerable subtropical regions.