Skillful seasonal prediction of Afro-Asian summer monsoon precipitation with a merged machine learning and large ensemble approach

Abstract

AbstractAfro-Asian summer monsoon precipitation (AfroASMP) is the life blood of billions of people living in many developing countries covering West Africa and Asia. Its complex variabilities are always accompanied by natural disasters like floods, landslides and droughts. Reliable AfroASMP prediction several months in advance is valuable for not only decision-makers but also regional socioeconomic sustainability. To address the current predicament of the AfroASMP seasonal prediction, this study provides an effective machine-learning model (Y-model). Y-model uses the monsoon related big climate data for searching the potential predictors, encompassing atmospheric internal factors and external forcings. Only the predictors associated with significant anomalies in summer horizonal winds at 850 hPa over the monsoon domain are retained. These selected predictors are then reorganized into a large ensemble based upon different thresholds of four fundamental principles. These principles include the focused sample sizes, the relationships between predictors and predictand, the independence among predictors, and the extremities of predictors in the forecast year. Real-time predictions can be generated based on the ensemble mean of skillful members during an independent hindcast period. Y-model skillfully predicts four monsoon precipitation indices of AfroASMP during 2011–2022 at lead 4–12 months, correlation skills range from 0.58 to 0.90 and root mean square error skills are reduced by 11–53% compared to CFS v2 model at lead 1 month. This study offers an effective method for preprocessing predictors in seasonal climate prediction.