Subseasonal Precipitation Forecasting in Southeastern Brazil Using Machine Learning with Explainable Artificial Intelligence - Costa, João Vyctor Ferreira da; Domingos, Matheus Corrêa; Santiago Júnior, Valdivino Alexandre de; Gomes, Jorge Luís (Anais do Workshop de Computação Aplicada à Gestão do Meio Ambiente e Recursos Naturais (WCAMA); 2026: Anais do XVII Workshop de Computação Aplicada à Gestão do Meio Ambiente e Recursos Naturais, 2026)
Subseasonal precipitation forecasting is essential for mitigating climate-related risks, but prediction remains challenging for traditional numerical weather prediction models. This study evaluates machine learning (ML) models—MLP, LSTM, GRU, and CNN-1D—for subseasonal precipitation forecasting in Southeastern Brazil. ERA5 reanalysis data (1991–2018) were used as the observational reference, with 1991–2016 for training and 2017–2018 for testing. The ML models were compared to the Brazilian Global Atmospheric Model (BAM), and the results show that all ML models outperform BAM, with MLP achieving the best overall performance. SHAP values were used to explain the MLP predictions, highlighting the importance of the input variables.