A Local-Remote AutoGRU Model for Long-Term Monthly Precipitation Forecasting in Asian Monsoon Region

Long-term precipitation forecasting is a critical issue for water resource management, disaster mitigation, and agricultural production. However, due to the uncertainty and dependency on global cycle events and various impacts, seasonal precipitation forecasting remains a challenge in terms of both lead time and accuracy. To address the problem, a Local-Remote AutoGRU model was developed based on the background of the Asian Monsoon Region (AMR). The structure of the model consists of four key components: screening and selection prediction factor from local and global cycle events; decomposition and extraction prediction features, including trends, periodicities, and correlations; employing the Gated Recurrent Unit (GRU) machine learning algorithm to explore the relationship between monthly precipitation and input series; and finally, composing and reconstructing the prediction series. This integrated strategy enables the prediction of monthly precipitation by leveraging local precipitation periodicity and tendency, global cycle events and grid location information. The results across 6.33 million km² Asian Monsoon Region demonstrated the proposed model’s remarkable performance. It achieved an overall NSE of 0.816 in the total area and all 12 lead months, representing a 21.69% accuracy improvement over baseline models. Additionally, the study revealed that the ENSO-related global cycle events play the primary drivers in the AMR, contributing 25.86–33.47% impacts to monthly precipitation in 7-10 months in advance, only next to the local precipitation periodicity. This study provides an effective approach for long-term monthly precipitation forecasting, particularly for the AMR.