DOI: 10.5281/zenodo.18234042
title: OSCARS - Regional State of the Climate: Data and Indices

description: This dataset provides harmonised regional climate data and climate-extreme indices developed within the framework of the Regional State of the Climate (RSOTC) project.
This work was supported by the European Union's Horizon Europe research and innovation programme through the OSCARS (Open Science Cluster of EOSC Regional State of the Climate) project, Grant Agreement No. 101058571.

The dataset uses the ERA5 reanalysis as its foundational data source. ERA5 is the fifth-generation atmospheric reanalysis produced by the European Centre for Medium-Range Weather Forecasts (ECMWF) and distributed via the Copernicus Climate Change Service (C3S)
(DOI: https://doi.org/10.24381/cds.4991cf48).

ERA5 provides hourly estimates of a wide range of atmospheric, land-surface, and sea-state variables at a spatial resolution of 0.25° x 0.25° (approximately 31 km), covering the period from 1940 to the present. These data form the basis for deriving the consistent and high-resolution regional climate information presented here.

The dataset provides regional information spatially aggregated as time series over European regions defined by the Nomenclature of Territorial Units for Statistics (NUTS) classification:



NUTS 0: Country level

NUTS 1: Major socio-economic regions

NUTS 2: Basic regions for regional policies

NUTS 3: Small regions for specific diagnoses


(Reference: Eurostat - NUTS classification)

The dataset is designed to support climate monitoring, impact assessment, policy analysis, and downstream climate services across multiple administrative scales. It is updated on a monthly basis.



Core variables

The core variables are derived from the state-of-the-art ERA5 reanalysis and represent fundamental quantities for climate and meteorological applications:





Near-surface air temperature (tas, tasmin, tasmax)
  Daily mean (tas), maximum (tasmax), and minimum (tasmin) air temperature at 2 m.




Total precipitation (pr)
  Daily accumulated liquid and frozen precipitation, relevant for hydrology, drought, and flood analysis.




10-m wind speed (sfcWind)
  Magnitude of the horizontal wind vector at 10 m, relevant for wind energy and atmospheric transport studies.





Derived indices

In addition to the core variables, the dataset includes a comprehensive set of derived climate indices commonly used in climate-impact and risk assessments. Indices are calculated for multiple temporal aggregations (annual, seasonal, and monthly).

Hot days



tx30: Number of days with maximum temperature (tasmax) > 30 °C  

tx35: Number of days with maximum temperature (tasmax) > 35 °C  

tx40: Number of days with maximum temperature (tasmax) > 40 °C  


Example: the tx35 value for January 2020 represents the number of days in that month with tasmax exceeding 35 °C.

Tropical nights



tr20: Number of nights with minimum temperature (tasmin) > 20 °C  

tr25: Number of nights with minimum temperature (tasmin) > 25 °C  


Frost days



fd: Number of frost days with minimum temperature (tasmin) < 0 °C  


Precipitation indicators



r1mm: Number of wet days (pr ≥ 1 mm)  

r20mm: Number of very heavy precipitation days (pr ≥ 20 mm)  

r95ptot: Total precipitation from very wet days (pr > 95th percentile)  


All indices follow established climate-index definitions, ensuring consistency and comparability across regions and time periods.



Data format

All datasets are stored as Zarr data stores and distributed as compressed ZIP archives for long-term archival and Zenodo compatibility.

Filenames follow the convention: 

{variable}_{pressure_level}_{dataset}_{region_set}.zarr.zip


For surface variables, the pressure level is specified as None.



Example usage

The following Python example demonstrates how to open a core variable and a derived index using xarray:

import xarray as xr

ds_tasmin = xr.open_dataset(
    "tasmin_None_ERA5_NUTS-3.zarr",
    engine="zarr"
)

ds_fd = xr.open_dataset(
    "fd_None_ERA5_NUTS-3.zarr",
    engine="zarr"
)

<xarray.Dataset>
Dimensions:  (region: 1345, time: 31412)
Coordinates:
  * region  (region) object 'CZ020' 'CZ031' ... 'NL327' 'NL328'
  * time    (time) datetime64[ns] 1940-01-01 ... 2025-12-31
Data variables:
    tasmin  (time, region) float64

<xarray.Dataset>
Dimensions:      (time: 1021, time_filter: 17, region: 1345)
Coordinates:
  * region       (region) object 'CZ020' ... 'NL328'
  * time         (time) datetime64[ns] 1940-01-01 ... 2025-01-01
  * time_filter  (time_filter) 'Annual' 'Apr' ... 'SepNov'
Data variables:
    fd           (time, time_filter, region) timedelta64[ns]




Core variables: daily data with time and region dimensions

Derived indices: aggregated data with an additional time_filter dimension indicating the aggregation period


Data production and reproducibility

All data products are generated using the open-source RSOTC Ingestion Pipeline, which implements a fully automated and reproducible workflow for data retrieval, preprocessing, aggregation, and index calculation.

The pipeline follows FAIR principles (Findable, Accessible, Interoperable, Reusable), ensuring transparent provenance, standardised metadata, and long-term usability.

Intended use

This dataset is intended for researchers, climate service providers, policymakers, and other stakeholders requiring consistent regional climate information across Europe. Typical use cases include:



Trend analysis

Regional climate diagnostics

Climate-impact indicators

Integration into dashboards and decision-support tools


Users are encouraged to cite this dataset when using it in scientific publications, reports, or operational climate services.