Longwang: Zero-Shot Global Spatiotemporal Precipitation Downscaling with a Latent Generative Prior

TL;DR

Longwang is a zero-shot generative model that enhances global precipitation downscaling from coarse to fine resolution, outperforming existing methods in accuracy and generalization.

Contribution

It introduces a novel latent generative prior framework that enables zero-shot downscaling of precipitation without region-specific training.

Findings

Outperforms standard posterior sampling in reconstructing spatial patterns.

Preserves temporal coherence and recovers extreme precipitation.

Generalizes well to climate simulations and projections.

Abstract

High-resolution precipitation information is essential for climate impact assessment, yet global climate models remain too coarse to resolve key small-scale processes. Existing machine learning downscaling methods often require paired low- and high-resolution data for supervised learning, are tied to fixed regions or scale factors during inference, and can be computationally expensive to train and run in physical space. Here we introduce Longwang, a zero-shot latent generative framework for global spatiotemporal precipitation downscaling. Longwang learns a context-conditioned latent generative prior and combines it with a physically informed observation operator through posterior sampling, enabling daily O(10 km) precipitation fields to be generated from monthly O(100 km) inputs. On ERA5 reanalysis, Longwang outperforms standard posterior sampling with an unconditional generative prior in reconstructing fine-scale spatial patterns, preserving temporal coherence, and recovering extreme precipitation intensities. The framework further generalizes to historical climate simulations and future climate projections under substantial distribution shift.