Multivariate climate downscaling with latent neural processes (Papers Track)

Anna Vaughan (Univeristy of Cambridge); Nic Lane (University of Cambridge); Michael Herzog (University of Cambridge)

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Climate Science & Modeling Causal & Bayesian Methods


Statistical downscaling is a vital tool in generating high resolution projections for climate impact studies. This study applies convolutional latent neural processes to multivariate downscaling of maximum temperature and precipitation. In contrast to existing downscaling methods, this model is shown to produce spatially coherent predictions at arbitrary locations specified at test time, regardless of whether training data are available at these points.