Advanced machine learning models have recently achieved high predictive accuracy for weather and climate prediction. However, these complex models often lack inherent transparency and interpretability, acting as "black boxes" that impede user trust and hinder further model improvements. As such, interpretable machine learning techniques have become crucial in enhancing the credibility and utility of weather and climate modeling. In this survey, we review current interpretable machine learning approaches applied to meteorological predictions. We categorize methods into two major paradigms: 1) Post-hoc interpretability techniques that explain pre-trained models, such as perturbation-based, game theory based, and gradient-based attribution methods. 2) Designing inherently interpretable models from scratch using architectures like tree ensembles and explainable neural networks. We summarize how each technique provides insights into the predictions, uncovering novel meteorological relationships captured by machine learning. Lastly, we discuss research challenges around achieving deeper mechanistic interpretations aligned with physical principles, developing standardized evaluation benchmarks, integrating interpretability into iterative model development workflows, and providing explainability for large foundation models.

Vast amounts of Earth System observations are available from satellites, ground-based weather stations, ships, planes and weather balloons. Recent advances in super-computing and machine learning, one form of artificial intelligence (AI), now make it possible for computer systems, "trained" from such data, to extract yet undiscovered information about the Earth System. Machine learning tools can not only learn the dynamics of complex features of the Earth System, features that are too complex for humans to understand, they are also able to use supercomputers more efficiently when compared to conventional tools. Currently, operational weather forecasts are derived from physically-based numerical models. The complexity of the Earth's weather means that simplifications must be made and skill in predicting weather drops off rapidly after just a few days.

Artificial intelligence is creating opportunities for contributing to much-needed efficiency gains in the handling of data that underpins Earth system science and weather and climate predictions, WMO Secretary-General Petteri Taalas told the Artificial Intelligence (AI) for Good Global Summit. The meeting, organized by the International Telecommunications Union, seeks to identify practical applications of artificial intelligence to advance the sustainable development agenda. It brings together more than 2,000 participants from over 120 countries. "This summit is the leading United Nations platform for dialogue on artificial intelligence. AI is being used to fight hunger, mitigate the climate crisis, or facilitate the transition to smart sustainable cities," said ITU Secretary-General Houlin Zhao.