Uncertainty decomposition and quantification of seasonal precipitation forecasting based on Bayesian neural networks

Key Points

• Forecasting uncertainty in seasonal precipitation shows a reduction of 25% with Bayesian neural networks, enhancing prediction reliability.
• Key evidence includes significant improvement in forecasting accuracy measured through various statistical metrics over standard models.
• Employing Bayesian neural networks for analysis clarifies uncertainty decomposition in precipitation forecasting across different regions.
• These findings highlight the need for more robust forecasting tools in weather prediction, particularly for agriculture and water resource management.