7  Downscaling and Bias Correction 🚧

Learning objectives

After reading this chapter, you should be able to:

• Distinguish between supervised and distributional downscaling approaches
• Understand the motivation for downscaling climate model outputs
• Apply bias correction and quantile-quantile mapping techniques
• Recognize the stationarity assumption and its implications
• Evaluate different downscaling methods for specific applications
• Understand modern machine learning approaches to climate downscaling

Further reading

• Lanzante et al. (2018) for comprehensive review of downscaling challenges
• Lafferty and Sriver (2023)
• Farnham, Doss-Gollin, and Lall (2018)

Farnham, David J, James Doss-Gollin, and Upmanu Lall. 2018. “Regional Extreme Precipitation Events: Robust Inference from Credibly Simulated GCM Variables.” Water Resources Research 54 (6). https://doi.org/10.1002/2017wr021318.

Lafferty, David C., and Ryan L. Sriver. 2023. “Downscaling and Bias-Correction Contribute Considerable Uncertainty to Local Climate Projections in CMIP6.” Npj Climate and Atmospheric Science 6 (1, 1): 1–13. https://doi.org/10.1038/s41612-023-00486-0.

Lanzante, John R, Keith W Dixon, Mary Jo Nath, Carolyn E Whitlock, and Dennis Adams-Smith. 2018. “Some Pitfalls in Statistical Downscaling of Future Climate.” Bulletin of the American Meteorological Society 99 (4): 791–803. https://doi.org/10.1175/bams-d-17-0046.1.