SeasGen is a project that uses generative artificial intelligence to predict climate conditions one season ahead. By learning from large climate simulation datasets, the system delivers fast and efficient seasonal forecasts that help organizations plan ahead in sectors affected by climate variability.
Many sectors, such as agriculture, energy, logistics and public planning, depend on knowing how climate conditions may change months in advance. However, current state-of-the-art climate prediction systems rely on traditional numerical models that require very large computing resources. This makes forecasts expensive to run and limits how often they can be produced and used in real operational settings.
SeasGen tackles this challenge by training generative AI models on large collections of climate simulations. These simulations are collected, cleaned and combined into a unified dataset suitable for deep learning. The AI model learns the patterns that cause seasonal climate anomalies and uses observations of the current climate state to generate predictions for the coming months. The results are presented through intuitive maps and visual summaries, allowing decision-makers to access seasonal climate information quickly and at a fraction of the computational cost of traditional models.
