Convolutional conditional neural processes for local climate downscaling

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Abstract A new model is presented for multisite statistical downscaling of temperature and precipitation using convolutional conditional neural processes (convCNPs). ConvCNPs are a recently developed class of models that allow deep-learning techniques to be applied to off-the-grid spatio-temporal data. In contrast to existing methods that map from low-resolution model output to high-resolution predictions at a discrete set of locations, this model outputs a stochastic process that can be queried at an arbitrary latitude–longitude coordinate. The convCNP model is shown to outperform an ensemble of existing downscaling techniques over Europe for both temperature and precipitation taken from the VALUE intercomparison project. The model also outperforms an approach that uses Gaussian processes to interpolate single-site downscaling models at unseen locations. Importantly, substantial improvement is seen in the representation of extreme precipitation events. These results indicate that the convCNP is a robust downscaling model suitable for generating localised projections for use in climate impact studies.

Location: Deutsche Nationalbibliothek Frankfurt am Main

Extent: Online-Ressource

Language: Englisch

Bibliographic citation: Convolutional conditional neural processes for local climate downscaling ; volume:15 ; number:1 ; year:2022 ; pages:251-268 ; extent:18
Geoscientific model development ; 15, Heft 1 (2022), 251-268 (gesamt 18)

Creator: Vaughan, Anna
Tebbutt, Will
Hosking, J. Scott
Turner, Richard E.

DOI: 10.5194/gmd-15-251-2022

URN: urn:nbn:de:101:1-2022012004280165579581

Rights: Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.

Last update: 15.08.2025, 7:20 AM CEST

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Vaughan, Anna
Tebbutt, Will
Hosking, J. Scott
Turner, Richard E.

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