A multivariate causal chain detection methodology assessed on the ENSO modulation of the Northeastern Brazil precipitation

This study presents a novel data-driven methodology for extraction causal chains from high-dimensional climate datasets, with a particular focus on detecting and visualizing the pathways that link climate anomalies across geographically distant regions. The proposed framework is designed to be computationally efficient while maintaining high interpretability, making it suitable for large-scale analytical applications in climate science. To assess its effectiveness, the methodology is applied to the well-studied teleconnection between the El Niño—Southern Oscillation and precipitation variability in Northeastern Brazil—a region highly susceptible to climate extremes such as droughts. The approach integrates clustering method, causal inference technique, and optimized path selection algorithm to construct interpretable causal chain graphs. Results demonstrate the methodology’s strong capability to identify key causal relationships and reproduce known climate phenomena. By effectively managing complex and massive climate data, this methodology contributes to the advancement of data science tools for climate analytics and offers valuable insights for understanding global climate teleconnections.