Multiscale Graph Neural Networks for Climate Data Analysis

We present a flexible deep learning framework for climate data analysis that leverages message-passing graph neural networks.

The framework is fully configurable and allows users to construct diverse architectures. In particular, it supports encoder-processor-decoder configurations in which geophysical fields are mapped onto a hierarchy of multi-icosahedral meshes, enabling information to propagate across scales before being mapped back to the original spatial grid. The model architecture is defined through a set of graph operators, including transformer-based graph convolutions. The framework operates on both regular and irregular grids, and enables flexible multivariate processing with spatial consistency. It further incorporates adaptive graph connectivity, enabling robust handling of missing data through dynamic edge construction. Additionally, several explainable AI (XAI) techniques are integrated to facilitate interpretation and physical attribution.

These features make the framework suitable for a broad range of climate and Earth-system applications, including data infilling, downscaling and process attribution. Its capabilities are illustrated through two case studies: (i) the reconstruction of global precipitation fields from incomplete observations, with comparison to established statistical and deep learning methods, and (ii) the attribution of large-scale drivers contributing to an extreme heatwave event.

The framework is currently being deployed as a web processing service that supports operational inference for selected climate applications.