Comment on nhess-2023-63

Abstract

<strong class="journal-contentHeaderColor">Abstract.</strong> There is a scientific consensus that the Mediterranean region (MEDR) is warming and as the temperature continues to rise, extreme events such as droughts and heat waves are becoming more frequent, severe, and widespread. Given the detrimental effects of droughts, it is crucial to accelerate the development of forecasting and early warning systems to minimize their negative impact. This paper examines the current state of knowledge in drought modeling and prediction using statistical, dynamical, and hybrid statistical-dynamical models, and suggests some research prospects to further improve drought prediction in this region. The review finds that while all methods have their strengths and shortcomings, hybrid statistical-dynamical methods can perform the most skillful prediction with a long lead time. However, the application of these methods is still challenging due to the lack of high-quality observational data and the limited computational resources. Finally, the paper concludes by discussing the importance of using a combination of sophisticated methods such as data assimilation techniques, machine learning models, and copula models and integrating data from different sources (e.g., remote sensing data, in-situ measurements, and reanalysis) to improve the accuracy and efficiency of drought forecasting.