Geoscientific Model Development
Geoscientific Model Development
GMD
Articles | Volume 15, issue 1
Articles | Volume 15, issue 1
https://doi.org/10.5194/gmd-15-251-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-15-251-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 15, issue 1
Model evaluation paper
 | 
13 Jan 2022
Model evaluation paper |  | 13 Jan 2022

Convolutional conditional neural processes for local climate downscaling

Anna Vaughan, Will Tebbutt, J. Scott Hosking, and Richard E. Turner

Viewed

Total article views: 5,574 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
3,852 1,625 97 5,574 120 137
  • HTML: 3,852
  • PDF: 1,625
  • XML: 97
  • Total: 5,574
  • BibTeX: 120
  • EndNote: 137
Views and downloads (calculated since 15 Mar 2021)
Cumulative views and downloads (calculated since 15 Mar 2021)

Viewed (geographical distribution)

Total article views: 5,574 (including HTML, PDF, and XML) Thereof 5,368 with geography defined and 206 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 
Latest update: 19 Nov 2025
Download
Short summary
We develop a new method for climate downscaling, i.e. transforming low-resolution climate model output to high-resolution projections, using a deep-learning model known as a convolutional conditional neural process. This model is shown to outperform an ensemble of baseline methods for downscaling daily maximum temperature and precipitation and provides a powerful new downscaling framework for climate impact studies.
Read more
Share