<p>The development of the coastal economy, coupled with global climate change, has gradually made drought one of humanity’s major disasters. It causes significant harm to both the natural environment and the socioeconomic development of coastal areas. This study aims to combine deep learning algorithms and CMIP6 models to build a prediction method for hydrological drought under climate change in this area. The changes of regional hydrological drought in mid-century and late-century were analyzed, and the hydrological drought was calculated using Standardized Runoff Index (SRI) at different time scales. And then we use the copula function to calculate the joint recurrence period of mid-century hydrological drought. In this study, a better simulation algorithm EMD-LSTM was constructed to predict the change of hydrological drought in the Dagu River Basin in mid-century and late-century. The results show that the relative change in annual precipitation varied from 11 to 25% compared with the historical averages across both the mid- and late-century periods. Meanwhile, monthly average temperatures were all higher than historical averages, with a maximum increase of 3.8&#xa0;°C (in the late-century under SSP5-8.5). The future trend of drought under different scenarios showed alternating dry and wet characteristics. Drought conditions were more severe in the mid-century compared to the end of the late-century. As the time scale increased, drought intensity decreased, but drought duration increased. This study provides a new potential method for accurate prediction of drought, offering a scientific basis for water resource management and protection.</p>