Abstract
Abstract
Accurately predicting hydroclimate events is crucial for understanding the impacts of climate change and effectively managing water resources, particularly for flood mitigation and timely warnings. Despite recent advances in machine learning, forecasting precipitation events continues to be challenging due to inherent data imbalances and the intricate dynamics governing these occurrences, rendering them difficult to model accurately. Echo State Networks (ESNs) offer a promising solution; their ability to model complex processes has been demonstrated throughout the field of environmental science. For this work, we propose a novel adaptation of ESNs, termed BinESN, to binary classification problems of precipitation occurrence. In particular, we extend the ESN to a generalized linear model framework, we leverage the ESN's ability to recognize complex dynamics while maintaining interpretability of the predicted output. Through simulation studies and an application to numerically simulated precipitation, we show that BinESN produces more accurate forecasts of sparse events in both short‐ and long‐range scenarios compared to other common machine learning approaches. Specifically, the proposed BinESN outperforms other reference methods by over 10% in terms of its area under the receiver operating characteristic curve.