| 28 Jul 2026
CMIP6-MedPlus dataset: climate projections for the Mediterranean region using statistical downscaling
Abstract. Global climate-model projections are often too coarse to represent the spatial variability required for regional assessments. This study presents CMIP6-MedPlus, an open-access climate projection dataset for an extended Mediterranean region, available at https://doi.org/10.5281/zenodo.17898529 (Todaro et al., 2025). It provides daily precipitation and near-surface air temperature at 0.25° spatial resolution for the period 1985–2100, based on five Coupled Model Intercomparison Project Phase 6 (CMIP6) Global Climate Models (GCMs) and two Shared Socioeconomic Pathways (SSP1-2.6 and SSP3-7.0). CMIP6-MedPlus was generated using a statistical downscaling framework that combines deep learning–based spatial refinement with bias correction. First, a Convolutional Neural Network (CNN)-based method was applied to enhance spatial resolution: ERA5 reanalysis fields were upscaled to the GCM resolution and used to train the CNN to learn the mapping between coarse- and fine-scale representations of the same variable. The trained CNN was then applied to GCM outputs to generate more spatially detailed fields. Second, quantile delta mapping was applied to the CNN-refined fields to correct systematic biases relative to ERA5 while preserving climate change signals. The resulting dataset provides spatially consistent regional climate information suitable for climate-impact across Mediterranean and neighbouring areas and serves as a basis for subsequent refinement at basin and local scales.
It's nice to see new papers on empirical-statistical downscaling, and I hope that my comments on it can lead to a discussion that further benefits the writing process.
Judging from the introduction of the paper, it doesn't span all the knowledge within the field of downscaling, and I think that it can be improved by a broader representation of past studies and experiences. One trick is to ask some LLM if the manuscript captures the broad field of downscaling. For instance, I've spent 25 years developing methods within empirical-statistical downscaling, but see that most of my efforts have been neglected by my peers. This is unfortunate, since I think we all have lots to learn from each other, from discussions, and from past experiences. There is less progress when part of the community is being ignored, and there are also some indications that studies end up with wrong conclusions if not all relevant work is accounted for (https://doi.org/10.1007/s00704-015-1597-5).
One comment about the three main frameworks: Perfect Prognosis (PP), Model Output Statistics (MOS) and Weather Generators (WGs) - there is also a fourth: the hybrid PP-MOS, as outlined in https://cordex.org/wp-content/uploads/2022/08/White-Paper-ESD.pdf. In addition, there are two main approaches: the traditional downscaling of data points (e.g. days, months) which can be described as "downscaling weather" and parameters describing the shape of the distribution curves (e.g. pdf or probabilities) which can be described as "downscaling climate" (these concepts are described in one of the appendices of https://doi.org/10.5194/hess-29-45-2025).
I find it useful to introduce the concept of models' minimum skillful scale in order to explain why we use the large-scale information from global climate models to infer changes in local small-scale conditions, which is explained in https://doi.org/10.2151/jmsj.2015-042. The minimum skillful scale also applies to regional climate models used in dynamical downscaling.
I will argue that Deep learning, artificial intelligence and machine learning (AI/ML) is not appropriate for downscaling climate projections (https://arxiv.org/abs/2601.00629), and that downscaling the statistical parameters describing the shape of statistical curves is a better way to capture changes in the statistics as well as extremes. Most AI/ML studies only compare their results to studies involving downscaling of weather and not downscaling of climate.
I don't see that MOS can be applied to coupled GCMs unless they are initialised to make predictions, but I think that the hybrid PP-MOS (which also is simple and elegant) is suitable for coupled GCM projections.
For AI/ML, the VALUE protocol for validation no longer is as suitable, since climate change will change how variables relate to each other internally. Also, bias correction should not be necessary when it comes to empirical-statistical downscaling, should it? To me, it's a red flag.