DOI: 10.5281/zenodo.17517423
title: CORDEX-ML-Bench: A benchmarking dataset for data-driven regional climate downscaling.

description: CORDEX-ML-Bench

A Standardized Benchmarking Dataset for Data-Driven Regional Climate Downscaling

📋 Overview

CORDEX-ML-Bench provides a standardized framework for evaluating machine learning models for regional climate downscaling. Participants will train models on Regional Climate Model (RCM) simulations and test their performance across different time periods and spatial domains. This Zenodo repository only provides the training data, other relevant information will be provided below. 

Key Features



RCM simulations spanning three distinct geographic domains. Allowing for testing and evaluation of emulators in multiple domains. Currently, this repository is in a testing phase, and not all the data has been uploaded. 

Evaluation framework across historical and future periods. CORDEX-ML-Bench provides tools for training and evaluating ESD and RCM emulators. These can be found here📦 WCRP-CORDEX/ml-benchmark.


🌍 Geographic Domains




Domain
Resolution
Target Variables
Target Grid Size
Predictor Variables
Predictor Grid Size
Static Fields




New Zealand (NZ)
0.11°
Tasmax, Pr
128 × 128
u, v, q, t, z at 850, 700, 500 hPa (15 variables)
16 x 16 (2°)
Orography (128 x 128; 0.11°)


Europe (ALPS)
0.11°
Tasmax, Pr
128 × 128
u, v, q, t, z at 850, 700, 500 hPa
16 x 16 (2°)
Orography




South Africa

(SA)

0.10°
Tasmax, Pr
128 × 128
u, v, q, t, z at 850, 700, 500 hPa 
16 x 16 (2°)
Orography




 

An illustration of the predictor (16 x 16)  and target domains (128 x 128)  are shown above. Noting that the predictor domains are slightly larger than the target domains. 

Predictor Variables (16 total, 16 x 16)



Atmospheric variables at 850 hPa, 700 hPa, 500 hPa :



u - zonal wind component

v - meridional wind component

q - specific humidity

t - temperature

z - geopotential height



Static field: Orography (topography)


🔧 Getting Started

Loading Data

For code examples and tutorials on loading the training data, refer to the official repository:

📦 WCRP-CORDEX/ml-benchmark

Data Preprocessing

Region-specific preprocessing information:



NZ Domain: nram812/CORDEXBench-nzdomain-preprocessing

ALPS Domain: jgonzalezab/CORDEXBench-alpsdomain-preprocessing


🎯 Training Framework

Perfect Framework Training

The benchmark investigates training in the "perfect" framework:



X (predictors): Coarsened RCM fields

y (targets): High-resolution truth from RCM


Evaluation is supported in both perfect (coarsened RCM) and imperfect (applied directly to the GCM) configurations.

🧪 Experimental Setups

1️⃣ ESD Pseudo-Reality (1961–1980)

Training Period: 20 years (1961–1980)GCM Examples: ACCESS-CM2 (NZ), CNRM-CM5 (ALPS)

This experiment mimics traditional Empirical Statistical Downscaling (ESD) training.

Configurations:



✅ With orography

❌ Without orography


2️⃣ Emulator Historical + Future (1961–1980 + 2081–2100)

Training Period: 40 years combinedPeriods: Historical (1961–1980) + Future (2081–2100)GCM Examples: ACCESS-CM2 (NZ), CNRM-CM5 (ALPS)

This experiment evaluates model extrapolation skill and transferability across different GCMs.

Configurations:



✅ With orography

❌ Without orography


📊 Evaluation Framework

Test Periods



Historical: 1981–2000

Mid-Century: 2041–2060

End-Century: 2081–2100


Evaluation Types



PP Cross-Validation: Same GCM used in training and testing

Imperfect Inference: Same GCM but different realizations or noise

Transferability Testing: Different GCM than training set

Change Signal Evaluation: Climate change response in future periods


📋 Evaluation Tables

Table 1: ESD "Pseudo-Reality" Evaluation (T1)




Training Setup
Inference Set
Evaluation Type
Notes
Metrics
Required




ESD 1961–1980<br>Static: Yes/No
Historical<br>(1981–2000)
PP cross-validation
Same GCM, perfectly
Error, Clim
✅


 
Historical<br>(1981–2000)
Imperfect cross-validation
Same GCM, imperfectly
Error, Clim
✅


 
2041–2060<br>2081–2100
Extrapolation
Same GCM, perfectly
Change signal
✅


 
2041–2060<br>2081–2100
Extrapolation
Same GCM, imperfectly
Change signal
✅




Table 2: Emulator Evaluation (T2)




Training Setup
Inference Set
Evaluation Type
Notes
Metrics
Required




Emulator<br>1961–1980 +<br>2081–2100<br>Static: Yes/No
Historical<br>(1981–2000)
PP cross-validation
Same GCM, perfectly
Error, Clim
✅


 
Historical<br>(1981–2000)
Imperfect cross-validation
Same GCM, imperfectly
Error, Clim
✅


 
Historical<br>(1981–2000)
 


Different GCM, 

perfectly

Error, Clim
✅


 
Historical<br>(1981–2000)
 


Different GCM, 

imperfectly

Error, Clim
✅


 
2041–2060<br>2081–2100
Extrapolation
Same GCM, perfectly
Change signal
✅


 
2041–2060<br>2081–2100
Extrapolation
Same GCM, imperfectly
Change signal
✅


 
2041–2060<br>2081–2100
Hard Transferability
Different GCM, perfectly
Change signal
✅


 
2041–2060<br>2081–2100
Hard Transferability
Different GCM, imperfectly
Change signal
✅




📁 Data Structure

New Zealand Domain

NZ_Domain/
├── train/
│   ├── ESD_pseudo-reality/
│   │   ├── predictors/
│   │   │   ├── ACCESS-CM2_1961-1980.nc
│   │   │   └── static.nc
│   │   └── target/
│   │       └── pr_tasmax_ACCESS-CM2_1961-1980.nc
│   │
│   └── Emulator_hist_future/
│       ├── predictors/
│       │   ├── ACCESS-CM2_1961-1980_2080-2099.nc
│       │   └── static.nc
│       └── target/
│           └── pr_tasmax_ACCESS-CM2_1961-1980_2080-2099.nc
│
└── test/
    ├── historical/
    │   ├── predictors/
    │   │   ├── perfect/
    │   │   │   ├── ACCESS-CM2_1981-2000.nc
    │   │   │   └── EC-Earth3_1981-2000.nc
    │   │   └── imperfect/
    │   │       ├── ACCESS-CM2_1981-2000.nc
    │   │       └── EC-Earth3_1981-2000.nc
    │   └── target/
    │       ├── pr_tasmax_ACCESS-CM2_1981-2000.nc
    │       └── pr_tasmax_EC-Earth3_1981-2000.nc
    │
    ├── mid_century/
    │   ├── predictors/
    │   │   ├── perfect/
    │   │   └── imperfect/
    │   └── target/
    │
    └── end_century/
        ├── predictors/
        │   ├── perfect/
        │   └── imperfect/
        └── target/


ALPS Domain

ALPS_Domain/
├── train/
│   ├── ESD_pseudo-reality/
│   │   ├── predictors/
│   │   │   ├── CNRM-CM5_1961-1980.nc
│   │   │   └── static.nc
│   │   └── target/
│   │       └── pr_tasmax_CNRM-CM5_1961-1980.nc
│   │
│   └── Emulator_hist_future/
│       ├── predictors/
│       │   ├── CNRM-CM5_1961-1980_2080-2099.nc
│       │   └── static.nc
│       └── target/
│           └── pr_tasmax_CNRM-CM5_1961-1980_2080-2099.nc
│
└── test/
    ├── historical/
    │   ├── predictors/
    │   │   ├── perfect/
    │   │   │   ├── CNRM-CM5_1981-2000.nc
    │   │   │   └── MPI-ESM-LR_1981-2000.nc
    │   │   └── imperfect/
    │   │       ├── CNRM-CM5_1981-2000.nc
    │   │       └── MPI-ESM-LR_1981-2000.nc
    │   └── target/
    │       ├── pr_tasmax_CNRM-CM5_1981-2000.nc
    │       └── pr_tasmax_MPI-ESM-LR_1981-2000.nc
    │
    ├── mid_century/
    │   ├── predictors/
    │   │   ├── perfect/
    │   │   └── imperfect/
    │   └── target/
    │
    └── end_century/
        ├── predictors/
        │   ├── perfect/
        │   └── imperfect/
        └── target/


🎓 Key Terminology



Perfect Inference: Using the same GCM realization that was used in training

Imperfect Inference: Using different realizations or added noise from the same GCM

PP Cross-Validation: Perfect-Prognosis evaluation with the same GCM

Hard Transferability: Testing on a completely different GCM than used in training

Change Signal: The difference between future and historical climate periods


📚 Resources



Main Repository: WCRP-CORDEX/ml-benchmark

NZ Preprocessing: CORDEXBench-nzdomain-preprocessing

ALPS Preprocessing: CORDEXBench-alpsdomain-preprocessing


Last Updated: November 2025