To what extent are AI-driven climate mod... - tlooto, The Most Powerful AcademicGPT

AI-driven models are rapidly enhancing our ability to predict extreme weather, particularly at short to medium lead times, by leveraging data-driven pattern recognition and efficient emulation of complex dynamics. Nevertheless, they remain best viewed as complementing—not replacing—traditional physics‐based approaches, especially when extrapolating beyond the training regimes or requiring clear causal explanations. 1. Improvements in Predictive Accuracy • Nowcasting and short‐term forecasts: Convolutional and recurrent networks excel at capturing localized features for minute‐to‐hour precipitation forecasts. Surveys report that deep architectures can automatically learn the key spatial patterns of rainfall extremes, heatwaves and severe convection, yielding performance on par with or exceeding operational numerical weather prediction (NWP) for lead times up to a few hours [1]. • Sub‐seasonal forecasts: A global ensemble of CNN‐based Deep Learning Weather Prediction (DLWP) models achiev