Downscaling & super-resolution
Evaluation of statistical downscaling methods for climate change projections over Spain: Future conditions with pseudo reality (transferability experiment)
In one sentence. Intercomparison of five statistical downscaling methods, including ANN, SVM and kernel ridge regression, reveals transferability problems for future climate over Spain.
Abstract
Abstract The Spanish Meteorological Agency (AEMET) is responsible for the elaboration of downscaled climate projections over Spain to feed the Second National Plan of Adaptation to Climate Change (PNACC‐2) and this is the last of three papers aimed to evaluate and intercompare five empirical/statistical downscaling (ESD) methods developed at AEMET: (a) Analog, (b) Regression, (c) Artificial Neural Networks, (d) Support Vector Machines and (e) Kernel Ridge Regression, in order to decide which methods and under what configurations are more suitable for that purpose. Following the framework established by the EU COST Action VALUE, in this experiment we test the transferability of these methods to future climate conditions with the use of regional climate models (RCMs) as pseudo observations. We evaluate the marginal aspects of the distributions of daily maximum/minimum temperatures and daily accumulated precipitation, over mainland Spain and the Balearic Islands, analysed by season. For maximum/minimum temperatures all methods display certain transferability issues, being remarkable for Support Vector Machines and Kernel Ridge Regression. For precipitation all methods appear to suffer from transferability difficulties as well, although conclusions are not as clear as for temperature, probably due to the fact that precipitation does not present such a marked signal of change. This study has revealed how an analysis over a historical period is not enough to fully evaluate ESD methods, so we propose that some type of analysis of transferability should be added in a standard procedure of a complete evaluation.
Labels
- Topic
- Downscaling & super-resolution, Benchmarks, datasets & evaluation
- Type
- Evaluation or intercomparison of existing models
- Method
- Classical ML (trees, GP, linear, kernels)
- Time scale
- Climate projections / centennial
- Domain
- Regional
- Data
- CMIP / climate model output
- Status
- Research
- Identifiers
- DOI:10.1002/joc.7464 OpenAlex:W3215637970
- Added
- 2026-10-22
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