From speed to trust: ensuring transparency in AI-based extreme weather forecasts

Artificial intelligence (AI) transforms extreme-weather forecasting by delivering faster and more accurate predictions at a fraction of the computational cost of traditional models. However, these advances are often accompanied by opaque decision processes, raising challenges for trust, equity, and long-term resilience in early warning systems. This article examines transparency in AI-based forecasting across three dimensions-predictive integrity, societal fairness, and long-term resilience-and argues that accuracy alone is insufficient in high-stakes contexts. Drawing on recent regulatory developments and global meteorological practice, we outline practical measures such as harmonized forecast labeling, impact-ready model cards, and extreme-event regulatory sandboxes. Embedding these measures within international frameworks is essential to ensure that the speed and efficiency of AI-driven forecasts translate into effective, trusted, and equitable early warning systems.