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A new model is presented for multisite statistical downscaling of temperatureand precipitation using convolutional conditional neural processes (convCNPs).ConvCNPs are a recently developed class of models that allow deep learningtechniques to be applied to off-the-grid spatio-temporal data. This model has asubstantial advantage over existing downscaling methods in that the trainedmodel can be used to generate multisite predictions at an arbitrary set oflocations, regardless of the availability of training data. The convCNP modelis shown to outperform an ensemble of existing downscaling techniques overEurope for both temperature and precipitation taken from the VALUEintercomparison project. The model also outperforms an approach that usesGaussian processes to interpolate single-site downscaling models at unseenlocations. Importantly, substantial improvement is seen in the representationof extreme precipitation events. These results indicate that the convCNP is arobust downscaling model suitable for generating localised projections for usein climate impact studies, and motivates further research into applications ofdeep learning techniques in statistical downscaling.

Convolutional conditional neural processes for local climate downscaling