The first high resolution CMIP6 dataset for Italy is now available, offering daily values for key climate variables, including temperature, wind speed, relative humidity and accumulated precipitation. Information that is crucial when implementing adaptation policies that rely on the latest scientific knowledge. “We have made a further step forward in understanding local climate trends in Italy and provide new detailed projections that allows researchers and decision-makers to improve their assessments of climate-related hazards, such as heatwaves, heavy rainfall, and droughts,” says CMCC researcher Paola Mercogliano. 

Italy’s complex topography makes it particularly challenging to capture climate variability on a local scale. However, the demand for detailed climate projections continues to increase with rising climate hazards and extreme events highlighting the need for improved methodologies that can capture climate variables.

In an effort to support the scientific community in advancing climate studies at the local scale, CMCC has applied a statistical downscaling approach, which allows for a computationally efficient and robust refinement of climate projections. This method has proven effective in providing high-resolution climate data which CMCC has made widely accessible through the Data Delivery System (DDS), in terms of raw climate data, and Dataclime platform providing maps for key climate indicators (some of which were also used in the Italy’s National Plan for Adaptation to Climate Change).

An example of CMCC’s data visualization map available on Dataclime, derived from the Statistical Downscaling of CMIP6 Climate Projection for Italy. The variation of Tropical Nights (TR; number of days per year with daily minimum temperature greater than 20°C) between the future period (2036-2065) and the reference period (1991-2020), considering the IPCC scenarios SSP1-2.6, is shown for the ensemble mean (derived by the TR indicators computed for each ensemble member).

“CMCC aims to support national policymakers, researchers, and industry professionals, by ensuring that climate information is not only scientifically sound but also readily available for practical applications,” says CMCC researcher and scientific leader of the Dataclime platform, Paola Mercogliano.

CMCC has published its latest dataset, the Statistical Downscaling through Empirical Quantile Mapping for an ensemble of Global Climate Models over ITaly (SD-EQM_GCMs_IT), which is available on CMCC DDS through the CMIP6 Stat Downscaled Over Italy tool and offers daily values at high-resolution (~5.5 km) for key climate variables, including mean, maximum, and minimum temperature, mean surface wind speed, mean relative humidity and accumulated precipitation for Italy covering the period from 1985 to 2100, including the historical experiment (1985-2014) and two different scenarios (2015-2100): SSP1-2.6 and SSP3-7.0. 

The climate variations maps, which are easy to access and use on the DATACLIME platform, following registration through the Statistical Downscaling of CMIP6 Climate Projections for Italy tool, provide effective visual cues and information on the expected climate variation under different IPCC scenarios.

Some of the key advantages to producing this new dataset and the related variation maps for selected climate indicators at national level include: the use of statistical downscaling, which is computationally less demanding than dynamic downscaling; access to this first high-resolution climate dataset based on the new CMIP6 scenarios for italian communities; hazard and impact assessment support through more up-to-date information that integrates CMIP6 scenarios with already available CMIP5 data; higher spatial and temporal resolution of CMIP6 global models; and improved representation of the climate system at local scales with more reliable assessments of climate variability and extremes.

Supporting climate studies with high-resolution, bias-corrected and accurate data is essential in addressing climate change challenges. In this context, statistical methods such as these allow for rapid generation of detailed climate information, complementing dynamical models and providing valuable climate information at the local scale. Furthermore CMCC is also working in the context of the EURO-CORDEX initiative and in coordination with CLM Assembly to also develop a dynamical downscaling of the same CMIP6 scenarios.

“This dataset provides significant improvement in the representation of  climate historical patterns for the different  variables investigated,” says CMCC researcher Giusy Fedele, who was also involved in the implementation of the service. “Furthermore, its availability represents an important step forward in understanding local climate trends in Italy and provides new detailed projections that allow researchers and decision-makers to improve their assessments of climate-related hazards, such as heatwaves, heavy rainfall, and droughts.”

This work was made possible through the collaborative effort of the global scientific community that developed the CMIP6 models, the Copernicus program made available the CMIP6 models and the CERRA dataset, and the EURO-CORDEX initiative, which has worked on advancing downscaling techniques in a coordinate way for many years.

Further information:

For more information on the dataset please visit the CMCC Data Delivery System dedicated page and the Dataclime platform.

Reder, A., Fedele, G., Manco, I. et al. Estimating pros and cons of statistical downscaling based on EQM bias adjustment as a complementary method to dynamical downscaling. Sci Rep 15, 621 (2025). https://doi.org/10.1038/s41598-024-84527-5

Fedele, G., Reder, A. and Mercogliano, P. Statistical Downscaling over Italy using EQM: CMIP6 Climate Projections for the 1985-2100 Period. (2025), submitted