  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 (GA-GWR). Our approach seeks to unveil
the explicit equations that govern the intricate relationship between
precipitation patterns and terrain characteristics in regions marked by complex
terrain. Through this AI-driven knowledge discovery, we uncover previously
undisclosed explicit equations that shed light on the connection between
terrain features and precipitation patterns. These 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. Ultimately, the unveiled equations have practical applications,
particularly in 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.
