Dunkelflaute, meaning "dark doldrums" in German, denotes prolonged periods characterized by low wind and solar energy production, attributed to overcast skies and calm weather conditions. With wind and solar power assuming increasingly crucial roles within the European energy landscape, these extreme weather conditions (Dunkelflaute events) pose a significant challenge to grid stability. Addressing this challenge in power production, the objective of this dissertation is to comprehensively analyze Dunkelflaute events and devise both physical and machine learning-based methodologies for their prediction. The research goal is approached through three key aspects: 1) conducting a statistical analysis of the frequency, duration, seasonal variations, and associated weather patterns of Dunkelflaute events to gain insights into their impact and underlying characteristics; 2) developing diverse strategies for the identification and prediction of these events from both a modelling and data availability standpoint to enhance predictability; and 3) projecting future weather patterns under climate change scenarios and investigate the impact of climate changes on this extreme weather....@en