# Fast and accurate learned multiresolution dynamical downscaling for precipitation arXiv: https://arxiv.org/abs/2101.06813 ยท HTML: https://mlsanity.com/paper/2101.06813/ ## Key facts - Cited by 18 papers in the map (data through 14 Oct 2026). - Builds on 10 of its 57 references that are in the map. - First posted on arXiv 18 Jan 2021. ## Builds on (most cited first, 10 of 10) - [Going Deeper with Convolutions](https://mlsanity.com/paper/1409.4842/) (2014) - [CBAM: Convolutional Block Attention Module](https://mlsanity.com/paper/1807.06521/) (2018) - [Conditional Generative Adversarial Nets](https://mlsanity.com/paper/1411.1784/) (2014) - [Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network](https://mlsanity.com/paper/1609.04802/) (2016) - [DeepSD: Generating High Resolution Climate Change Projections through Single Image Super-Resolution](https://mlsanity.com/paper/1703.03126/) (2017) - [Stochastic Super-Resolution for Downscaling Time-Evolving Atmospheric Fields with a Generative Adversarial Network](https://mlsanity.com/paper/2005.10374/) (2020) - [TomoGAN: Low-Dose Synchrotron X-Ray Tomography with Generative Adversarial Networks](https://mlsanity.com/paper/1902.07582/) (2019) - [BraggNN: Fast X-ray Bragg Peak Analysis Using Deep Learning](https://mlsanity.com/paper/2008.08198/) (2020) - [Deep Learning Accelerated Light Source Experiments](https://mlsanity.com/paper/1910.04081/) (2019) - [Scientific Image Restoration Anywhere](https://mlsanity.com/paper/1911.05878/) (2019) ## Built on by (most cited first, 18 of 18) - [Foundation Models for Weather and Climate Data Understanding: A Comprehensive Survey](https://mlsanity.com/paper/2312.03014/) (2023) - [Hard-Constrained Deep Learning for Climate Downscaling](https://mlsanity.com/paper/2208.05424/) (2022) - [Generative Diffusion-based Downscaling for Climate](https://mlsanity.com/paper/2404.17752/) (2024) - [AI Foundation Models for Weather and Climate: Applications, Design, and Implementation](https://mlsanity.com/paper/2309.10808/) (2023) - [Can AI be enabled to dynamical downscaling? A Latent Diffusion Model to mimic km-scale COSMO5.0CLM9 simulations](https://mlsanity.com/paper/2406.13627/) (2024) - [Using Explainability to Inform Statistical Downscaling Based on Deep Learning Beyond Standard Validation Approaches](https://mlsanity.com/paper/2302.01771/) (2023) - [Multi-variable Hard Physical Constraints for Climate Model Downscaling](https://mlsanity.com/paper/2308.01868/) (2023) - [Efficient Kilometer-Scale Precipitation Downscaling with Conditional Wavelet Diffusion](https://mlsanity.com/paper/2507.01354/) (2025) - [Rotationally Equivariant Super-Resolution of Velocity Fields in Two-Dimensional Fluids Using Convolutional Neural Networks](https://mlsanity.com/paper/2202.11099/) (2022) - [Hybrid Neural Representations for Spherical Data](https://mlsanity.com/paper/2402.05965/) (2024) - [Multi-BVOC Super-Resolution Exploiting Compounds Inter-Connection](https://mlsanity.com/paper/2305.14180/) (2023) - [Statistical treatment of convolutional neural network super-resolution of inland surface wind for subgrid-scale variability quantification](https://mlsanity.com/paper/2211.16708/) (2022) - [Wasserstein GAN-Based Precipitation Downscaling with Optimal Transport for Enhancing Perceptual Realism](https://mlsanity.com/paper/2507.17798/) (2025) - [Probabilistic Super-Resolution for Urban Micrometeorology via a Schr"odinger Bridge](https://mlsanity.com/paper/2510.12148/) (2025) - [Leveraging Land Cover Priors for Isoprene Emission Super-Resolution](https://mlsanity.com/paper/2503.18658/) (2025) - [Diffusion-Based, Data-Assimilation-Enabled Super-Resolution of Hub-height Winds](https://mlsanity.com/paper/2510.03364/) (2025) - [Earth Science Foundation Models: From Perception to Reasoning and Discovery](https://mlsanity.com/paper/2605.12542/) (2026) - [Regional Climate Model Emulation with Diffusion Approaches: What is the Added Value of Generative Machine Learning?](https://mlsanity.com/paper/2606.14570/) (2026) ## Abstract (arXiv) This study develops a neural network-based approach for emulating high-resolution modeled precipitation data with comparable statistical properties but at greatly reduced computational cost. The key idea is to use combination of low- and high- resolution simulations to train a neural network to map from the former to the latter. Specifically, we define two types of CNNs, one that stacks variables directly and one that encodes each variable before stacking, and we train each CNN type both with a conventional loss function, such as mean square error (MSE), and with a conditional generative adversarial network (CGAN), for a total of four CNN variants. We compare the four new CNN-derived high-resolution precipitation results with precipitation generated from original high resolution simulations, a bilinear interpolater and the state-of-the-art CNN-based super-resolution (SR) technique. Results show that the SR technique produces results similar to those of the bilinear interpolator with smoother spatial and temporal distributions and smaller data variabilities and extremes than the original high resolution simulations. While the new CNNs trained by MSE generate better results over some regions than the interpolator and SR technique do, their predictions are still not as close as the original high resolution simulations. The CNNs trained by CGAN generate more realistic and physically reasonable results, better capturing not only data variability in time and space but also extremes such as intense and long-lasting storms. The new proposed CNN-based downscaling approach can downscale precipitation from 50~km to 12~km in 14~min for 30~years once the network is trained (training takes 4~hours using 1~GPU), while the conventional dynamical downscaling would take 1~month using 600 CPU cores to generate simulations at the resolution of 12~km over contiguous United States.