  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. This model has a
substantial advantage over existing downscaling methods in that the trained
model can be used to generate multisite predictions at an arbitrary set of
locations, regardless of the availability of training data. 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, and motivates further research into applications of
deep learning techniques in statistical downscaling.
