Statistical downscaling

Statistical downscaling estimates local or regional climate conditions from large-scale atmospheric predictors using empirical, statistical, or machine-learning relationships.

Why it matters

Compared with Dynamical downscaling, statistical downscaling is computationally cheap and can be attractive when long ensembles or many scenarios are needed. Its main limitation is that it assumes the predictor-response relationships remain valid under changed climate conditions.

Typical use

• Translate coarse reanalysis or GCM output into local climate indicators.
• Correct large-scale biases using station observations.
• Support impact studies when fully dynamical simulations are not feasible.

See also: Regional climate modeling, Dynamical downscaling, Convection-permitting modeling