Neural Network Postprocessing of Long-Range Forecasts of Temperature and Precipitation in the Czech Republic

Seasonal climate forecasts are increasingly integrated into climate services and risk-based decision-making across sectors such as energy, agriculture, and water management. Their value lies not only in predicting mean conditions but in providing probabilistic information about anomalies and extremes on seasonal timescales. However, their skill remains highly region- and variable-dependent. In Central Europe, predictive skill is generally modest and particularly limited for precipitation, which is strongly influenced by internal atmospheric variability and small-scale processes that are not fully resolved by global models. These limitations highlight the need for advanced postprocessing techniques to extract usable local-scale information.

This study investigates the potential of neural network–based statistical postprocessing to improve seasonal forecasts of near-surface air temperature and precipitation in the Czech Republic. The approach is based on empirical relationships between local observations and large-scale predictors derived from global seasonal forecast systems.

We use hindcast data from the Copernicus Climate Change Service (C3S), focusing on four forecast systems: ECMWF, Météo-France, DWD, and CMCC. Predictor variables include near-surface air temperature, sea level pressure, and selected large-scale circulation fields relevant for precipitation variability. Observational gridded datasets derived from station measurements serve as the reference for both temperature and precipitation.

The analysis covers the common hindcast period 1993–2016 over the Czech Republic. Neural networks are applied as a nonlinear postprocessing tool to capture complex relationships between predictors and local climate variables. Forecast performance is evaluated using categorical verification (above-normal, normal, below-normal conditions) for both temperature and precipitation.

Results indicate that neural network postprocessing improves forecast skill for temperature, particularly in winter months and at shorter lead times, while improvements for precipitation remain limited. These results demonstrate the potential of neural networks to enhance seasonal prediction skill in Central Europe, while also highlighting their limitations.