# Convolutional conditional neural processes for local climate downscaling

arXiv: https://arxiv.org/abs/2101.07950 · HTML: https://mlsanity.com/paper/2101.07950/

## Key facts

- Cited by 29 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 80 references that are in the map.
- First posted on arXiv 20 Jan 2021.

## Builds on (most cited first, 17 of 17)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Fourier Neural Operator for Parametric Partial Differential Equations](https://mlsanity.com/paper/2010.08895/) (2020)
- [Xception: Deep Learning with Depthwise Separable Convolutions](https://mlsanity.com/paper/1610.02357/) (2016)
- [Variational Inference with Normalizing Flows](https://mlsanity.com/paper/1505.05770/) (2015)
- [Neural Operator: Graph Kernel Network for Partial Differential Equations](https://mlsanity.com/paper/2003.03485/) (2020)
- [Conditional Neural Processes](https://mlsanity.com/paper/1807.01613/) (2018)
- [Neural Processes](https://mlsanity.com/paper/1807.01622/) (2018)
- [Attentive Neural Processes](https://mlsanity.com/paper/1901.05761/) (2019)
- [Convolutional Conditional Neural Processes](https://mlsanity.com/paper/1910.13556/) (2019)
- [DeepSD: Generating High Resolution Climate Change Projections through Single Image Super-Resolution](https://mlsanity.com/paper/1703.03126/) (2017)
- [Sequential Neural Processes](https://mlsanity.com/paper/1906.10264/) (2019)
- [ClimAlign: Unsupervised statistical downscaling of climate variables via normalizing flows](https://mlsanity.com/paper/2008.04679/) (2020)
- [Intercomparison of Machine Learning Methods for Statistical Downscaling: The Case of Daily and Extreme Precipitation](https://mlsanity.com/paper/1702.04018/) (2017)
- [Recurrent Attentive Neural Process for Sequential Data](https://mlsanity.com/paper/1910.09323/) (2019)
- [Quantifying Uncertainty in Discrete-Continuous and Skewed Data with Bayesian Deep Learning](https://mlsanity.com/paper/1802.04742/) (2018)
- [Downscaling near-surface atmospheric fields with multi-objective Genetic Programming](https://mlsanity.com/paper/1407.1768/) (2014)

## Built on by (most cited first, 29 of 29)

- [ClimaX: A foundation model for weather and climate](https://mlsanity.com/paper/2301.10343/) (2023)
- [A Generative Deep Learning Approach to Stochastic Downscaling of Precipitation Forecasts](https://mlsanity.com/paper/2204.02028/) (2022)
- [Autoregressive Conditional Neural Processes](https://mlsanity.com/paper/2303.14468/) (2023)
- [Practical Conditional Neural Processes Via Tractable Dependent Predictions](https://mlsanity.com/paper/2203.08775/) (2022)
- [Spectral Diffusion Processes](https://mlsanity.com/paper/2209.14125/) (2022)
- [Deconditional Downscaling with Gaussian Processes](https://mlsanity.com/paper/2105.12909/) (2021)
- [The Neural Process Family: Survey, Applications and Perspectives](https://mlsanity.com/paper/2209.00517/) (2022)
- [Environmental Sensor Placement with Convolutional Gaussian Neural Processes](https://mlsanity.com/paper/2211.10381/) (2022)
- [Using Explainability to Inform Statistical Downscaling Based on Deep Learning Beyond Standard Validation Approaches](https://mlsanity.com/paper/2302.01771/) (2023)
- [Memory Efficient Neural Processes via Constant Memory Attention Block](https://mlsanity.com/paper/2305.14567/) (2023)
- [FNP: Fourier Neural Processes for Arbitrary-Resolution Data Assimilation](https://mlsanity.com/paper/2406.01645/) (2024)
- [Sim2Real for Environmental Neural Processes](https://mlsanity.com/paper/2310.19932/) (2023)
- [Spectral Convolutional Conditional Neural Processes](https://mlsanity.com/paper/2404.13182/) (2024)
- [Real-time Adaptation for Condition Monitoring Signal Prediction using Label-aware Neural Processes](https://mlsanity.com/paper/2403.16377/) (2024)
- [On the use of Deep Generative Models for Perfect Prognosis Climate Downscaling](https://mlsanity.com/paper/2305.00974/) (2023)
- [Exploiting Inferential Structure in Neural Processes](https://mlsanity.com/paper/2306.15169/) (2023)
- [Aardvark weather: end-to-end data-driven weather forecasting](https://mlsanity.com/paper/2404.00411/) (2024)
- [Neural Stochastic Processes for Satellite Precipitation Refinement](https://mlsanity.com/paper/2604.10414/) (2026)
- [MoCoLSK: Modality Conditioned High-Resolution Downscaling for Land Surface Temperature](https://mlsanity.com/paper/2409.19835/) (2024)
- [Are Deep Learning Methods Suitable for Downscaling Global Climate Projections? An Intercomparison for Temperature and Precipitation over Spain](https://mlsanity.com/paper/2411.05850/) (2024)
- [Scalable Spatiotemporal Inference with Biased Scan Attention Transformer Neural Processes](https://mlsanity.com/paper/2506.09163/) (2025)
- [Advancing Ocean State Estimation with efficient and scalable AI](https://mlsanity.com/paper/2511.06041/) (2025)
- [Optimising antibiotic switching via forecasting of patient physiology](https://mlsanity.com/paper/2603.08242/) (2026)
- [From Drops to Grid: Noise-Aware Spatio-Temporal Neural Process for Rainfall Estimation](https://mlsanity.com/paper/2605.05912/) (2026)
- [Revisiting Neural Processes via Fourier Transform and Volterra Series](https://mlsanity.com/paper/2606.01172/) (2026)
- [Regional Climate Model Emulation with Diffusion Approaches: What is the Added Value of Generative Machine Learning?](https://mlsanity.com/paper/2606.14570/) (2026)
- [Domain-Adaptive Climate Downscaling Under Temporal Distribution Shift](https://mlsanity.com/paper/2607.05645/) (2026)
- [OmniPMNet: Bridging discrete and gridded PM10 forecasts via omni-query neural processes](https://mlsanity.com/paper/2607.11896/) (2026)
- [Earth observation embeddings are effective sub-grid descriptors for probabilistic weather downscaling](https://mlsanity.com/paper/2608.12271/) (2026)

## Abstract (arXiv)

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.