Statistical climate downscaling in Python

Downscaling & bias correction of CMIP6 tasmin, tasmax, and pr for the R/CIL GDPCIR project

The Super-Resolution for Renewable Resource Data (sup3r) software uses generative adversarial networks to create synthetic high-resolution wind and solar spatiotemporal data from coarse low-resolution inputs.

Deep Learning for empirical DownScaling. Python package with state-of-the-art and novel deep learning algorithms for empirical/statistical downscaling of gridded data

A project on how to incorporate physics constraints into deep learning architectures for downscaling or other super--resolution tasks.

The Intermediate Complexity Atmospheric Research model (ICAR)

Scale down / "pause" Kubernetes workload (Deployments, StatefulSets, and/or HorizontalPodAutoscalers and CronJobs too !) during non-work hours.

Diffusion for climate downscaling

A horizontal autoscaler for Kubernetes workloads

Python Package for Empirical Statistical Downscaling. pyESD is under active development and all colaborators are welcomed. The purpose of the package is to downscale any climate variables e.g. precipitation and temperature using predictors from reanalysis datasets (eg. ERA5) to point scale. pyESD adopts many ML and AL as the transfer function.

TopoPyScale: a Python library to perform simplistic climate downscaling at the hillslope scale

spateGAN-ERA5: deep learning based spatio-temporal downscaling of ERA5 precipitation

Generalized Analog Regression Downscaling (GARD) code

Probabilistic Downscaling of Climate Variables Using Denoising Diffusion Probabilistic Models

Python tool for downsizing Microsoft PowerPoint presentations (pptx) files.

Statistical dowscaling of climate data at daily scale using quantile mapping (QPM) technique.

[ICCV 2025] - Image Intrinsic Scale Assessment: Bridging the Gap Between Quality and Resolution

A project on how to incorporate physics constraints into deep learning architectures for downscaling or other super--resolution tasks.

Generate stocastic Gaussian realization constrained to a coarse scale image.

Given a global mean temperature pathway, generate random global climate fields consistent with it and with spatial and temporal correlation derived from an ESM

Awesome-AI4Earth: a curated list of machine learning in Earth System, especially for weather and climate.

Code repository associated with "Statistical treatment of convolutional neural network super-resolution of inland surface wind for subgrid-scale variability quantification" (Getter, Bessac, Rudi, Feng).

Python package to reconstruct and extend observational climate series through empricial downscaling of large-scale models

Cost saving K8s controller to scale down and up of resources during non-business hours

A collection of notebooks and tools for analyzing the LOCA dataset

Weather Generators with Bayesian Networks

Downscaling spatial resolution of geo-spatial data (TROPOMI SIF) using auxiliary data (MODIS) by application of U-NET and Local Binary Patterns

The official AtmoSwing repository

r package doing Simple Quantile Mapping downscaling technique.

This repository contains three packages that assemble codes and scripts to downscale coarse-resolution reanalysis fields to finer resolutions, accounting for subgrid-scale variability and/or topographic effects.