Exploring Terrain-Precipitation Relationships with Interpretable AI for Advancing Future Climate Projections

Despite the remarkable strides made by AI-driven models in modern precipitation forecasting, these black-box models cannot inherently deepen the comprehension of underlying mechanisms. To address this limitation, we propose an AI-driven knowledge discovery framework known as genetic algorithm-geographic weighted regression. Through this framework, we have constructed an iterative optimization of knowledge generation and utilization. On the one hand, new explicit equations are discovered to describe the intricate relationship between precipitation patterns and terrain characteristics. Experiments have shown that the discovered equations demonstrate remarkable accuracy when applied to precipitation data, outperforming conventional empirical models. Notably, our research reveals that the parameters within these equations are dynamic, adapting to evolving climate patterns. On the other hand, these previously undisclosed equations have contributed new knowledge about terrain-precipitation relationships, which can be embedded into the AI model for better interpretability and climate projection accuracy. Specifically, the unveiled equations can enable fine-scale downscaling for precipitation predictions using low-resolution future climate data. This capability offers invaluable insights into the anticipated changes in precipitation patterns across diverse terrains under future climate scenarios, which enhances our ability to address the challenges posed by contemporary climate science.