# Intercomparison of Machine Learning Methods for Statistical Downscaling: The Case of Daily and Extreme Precipitation

arXiv: https://arxiv.org/abs/1702.04018 · HTML: https://mlsanity.com/paper/1702.04018/

## Key facts

- Cited by 24 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Feb 2017.

## Built on by (most cited first, 24 of 24)

- [ClimaX: A foundation model for weather and climate](https://mlsanity.com/paper/2301.10343/) (2023)
- [ClimateLearn: Benchmarking Machine Learning for Weather and Climate Modeling](https://mlsanity.com/paper/2307.01909/) (2023)
- [Debias Coarsely, Sample Conditionally: Statistical Downscaling through Optimal Transport and Probabilistic Diffusion Models](https://mlsanity.com/paper/2305.15618/) (2023)
- [Convolutional conditional neural processes for local climate downscaling](https://mlsanity.com/paper/2101.07950/) (2021)
- [Practical Conditional Neural Processes Via Tractable Dependent Predictions](https://mlsanity.com/paper/2203.08775/) (2022)
- [Generative Diffusion-based Downscaling for Climate](https://mlsanity.com/paper/2404.17752/) (2024)
- [Machine learning emulation of a local-scale UK climate model](https://mlsanity.com/paper/2211.16116/) (2022)
- [Bridging observation, theory and numerical simulation of the ocean using Machine Learning](https://mlsanity.com/paper/2104.12506/) (2021)
- [Machine learning emulation of precipitation from km-scale UK regional climate simulations using a diffusion model](https://mlsanity.com/paper/2407.14158/) (2024)
- [A Generative Framework for Probabilistic, Spatiotemporally Coherent Downscaling of Climate Simulation](https://mlsanity.com/paper/2412.15361/) (2024)
- [Using Explainability to Inform Statistical Downscaling Based on Deep Learning Beyond Standard Validation Approaches](https://mlsanity.com/paper/2302.01771/) (2023)
- [Observation-Guided Meteorological Field Downscaling at Station Scale: A Benchmark and a New Method](https://mlsanity.com/paper/2401.11960/) (2024)
- [Analysis, Characterization, Prediction and Attribution of Extreme Atmospheric Events with Machine Learning: a Review](https://mlsanity.com/paper/2207.07580/) (2022)
- [An attention mechanism based convolutional network for satellite precipitation downscaling over China](https://mlsanity.com/paper/2203.14812/) (2022)
- [Downscaling Extreme Rainfall Using Physical-Statistical Generative Adversarial Learning](https://mlsanity.com/paper/2212.01446/) (2022)
- [IndiaWeatherBench: A Dataset and Benchmark for Data-Driven Regional Weather Forecasting over India](https://mlsanity.com/paper/2509.00653/) (2025)
- [China Regional 3km Downscaling Based on Residual Corrective Diffusion Model](https://mlsanity.com/paper/2512.05377/) (2025)
- [A unified multimodal understanding and generation model for cross-disciplinary scientific research](https://mlsanity.com/paper/2601.01363/) (2026)
- [Self Supervised Vision for Climate Downscaling](https://mlsanity.com/paper/2401.09466/) (2024)
- [Investigating the Robustness of Extreme Precipitation Super-Resolution Across Climates](https://mlsanity.com/paper/2507.09166/) (2025)
- [Downscaling human mobility data based on demographic socioeconomic and commuting characteristics using interpretable machine learning methods](https://mlsanity.com/paper/2509.21703/) (2025)
- [Assessing the risk of future Dunkelflaute events for Germany using generative deep learning](https://mlsanity.com/paper/2509.24788/) (2025)
- [Deep Learning-Based Statistical Downscaling of Sea Surface Temperature Using a Residual Corrective Neural Network](https://mlsanity.com/paper/2608.10022/) (2026)
- [Precipitation Downscaling Using Foundation Model-Conditioned Diffusion](https://mlsanity.com/paper/2608.25858/) (2026)

## Abstract (arXiv)

Statistical downscaling of global climate models (GCMs) allows researchers to study local climate change effects decades into the future. A wide range of statistical models have been applied to downscaling GCMs but recent advances in machine learning have not been explored. In this paper, we compare four fundamental statistical methods, Bias Correction Spatial Disaggregation (BCSD), Ordinary Least Squares, Elastic-Net, and Support Vector Machine, with three more advanced machine learning methods, Multi-task Sparse Structure Learning (MSSL), BCSD coupled with MSSL, and Convolutional Neural Networks to downscale daily precipitation in the Northeast United States. Metrics to evaluate of each method's ability to capture daily anomalies, large scale climate shifts, and extremes are analyzed. We find that linear methods, led by BCSD, consistently outperform non-linear approaches. The direct application of state-of-the-art machine learning methods to statistical downscaling does not provide improvements over simpler, longstanding approaches.