Subseasonal Ensemble Prediction of the 2024 Abrupt Drought-to-Flood Transition in Henan Province, China

In 2024, an abrupt drought-to-flood transition (ADFT) event occurred in Henan Province, China, causing severe losses to agriculture and the economy. Predicting the spatiotemporal evolution of such compound extremes remains challenging at the subseasonal scale. This study employs soil moisture percentiles to identify local droughts and floods, connects them into coherent patches, and detects an ADFT event spatiotemporally. The proposed three-dimensional identification method is further applied to evaluate the ECMWF S2S reforecasts of the 2024 ADFT event. At a 1-week lead, the ECMWF ensemble mean successfully captures the transition. However, the spatial extent is underpredicted substantially at a 2-week lead. In terms of probabilistic forecast, the Brier skill scores for drought, transition, and flood stages are 0.38, 0.57, and 0.38 at a 1-week lead, respectively. However, these scores drop sharply at a 2-week lead, particularly for the transition and flood stages. The decreased forecast skill is jointly influenced by internal dynamical errors in the model and biases in the positions of the subtropical high- and low-pressure systems at long lead. This study assesses the capability of a numerical model to predict a compound extreme from both deterministic and probabilistic perspectives, and highlights the critical role of atmospheric circulation in achieving skillful prediction.