Earth system models (ESMs) are our main tools for quantifying the physical state of the Earth and predicting how it might change in the future under ongoing anthropogenic forcing. In recent years, however, artificial intelligence (AI) methods have been increasingly used to augment or even replace classical ESM tasks, raising hopes that AI could solve some of the grand challenges of climate science. In this Perspective we survey the recent achievements and limitations of both process-based models and AI in Earth system and climate research, and propose a methodological transformation in which deep neural networks and ESMs are dismantled as individual approaches and reassembled as learning, self-validating and interpretable ESM–network hybrids. Following this path, we coin the term neural Earth system modelling. We examine the concurrent potential and pitfalls of neural Earth system modelling and discuss the open question of whether AI can bolster ESMs or even ultimately render them obsolete.

地球系统模型 (ESM) 是我们量化地球物理状态和预测未来在持续的人为强迫下可能发生的变化的主要工具。然而，近年来，人工智能 (AI) 方法越来越多地用于增强甚至取代经典的 ESM 任务，这让人们希望 AI 能够解决气候科学的一些重大挑战。在这个视角中，我们调查了基于过程的模型和人工智能在地球系统和气候研究中的最新成就和局限性，并提出了一种方法论转变，其中深度神经网络和 ESM 被分解为单独的方法并重新组装为学习、自我验证和可解释的 ESM-网络混合。沿着这条道路，我们创造了术语神经地球系统建模。