A Driver‐Aware Machine Learning Approach for Predicting the Severity of Decadal‐Scale Extreme Climate Events

Abstract

The prediction of the decadal‐scale severities of extreme wet and dry events requires long‐term records of data to capture long‐term fluctuations. Recently, purely data‐driven machine learning models have shown excellent performance in modelling precipitation and other water cycle variables when sufficiently long and comprehensive training datasets are available, but their predictive reliability may be limited when long training records are unavailable. To better understand and predict extreme climate event severities at the basin scale, we propose an integrated framework coupling driver analysis and machine learning methods. Combined with the driver analysis method, the long short‐term memory (LSTM) model and the support vector regression (SVR) model are jointly used to predict extreme event severities in the Yellow River Basin (YRB) in the next decade. The results showed that extreme dry events are mainly driven by the long‐term signals of global warming, the Atlantic Multidecadal Oscillation (AMO), and the Pacific Decadal Oscillation (PDO) in the YRB. Extreme wet events are dominated by the long‐term signals of the Southern Oscillation Index (SOI), global warming, and the PDO. In 2030, the spatial distribution patterns of both extreme dry event severities and extreme wet event severities in the YRB will potentially be more scattered. Compared to 2020, there will be at most 30% increases and at most 20% decreases in extreme dry event severity in 2030 across the YRB, with a maximum increase in extreme wet event severity of 60% and a maximum decrease of approximately 20% in 2030. The proposed framework provides a way to incorporate information on large‐scale climate drivers into data‐driven prediction models.