An AI-based framework for high-resolution climate dataset over Italy: from historical reconstruction to an operational chain

High-resolution climate datasets are fundamental for monitoring extreme events, assessing climate variability, and supporting climate adaptation strategies. However, producing high-resolution climate reanalyses usually requires computationally expensive dynamical downscaling. As a result, near–real-time high-resolution climate services remain limited, since most downscaling products are generated retrospectively with delays of months to years (Hersbach et al., 2020; Harris et al., 2022). Recent advances in generative machine learning enable realistic fine-scale atmospheric fields that preserve spatial coherence and key statistics, including extremes (Rampal et al., 2025; Camps-Valls et al., 2025). Hybrid statistical–dynamical approaches therefore provide an efficient and physically consistent pathway for operational high-resolution dataset production (Glawion et al., 2025; Schmidt et al., 2025). This work presents the progress achieved in the development of a high-resolution climate datasets over the Italian Peninsula at 2.2 km resolution, exploiting a conditional Generative Adversarial Network (cGAN) model developed in Manco et al. (2025). The framework follows a hybrid statistical–dynamical downscaling strategy, in which ERA5 reanalysis data at 0.25° resolution are downscaled using cGANs trained against the very-high-resolution dynamical product VHR-REA_IT (Raffa et al., 2021). The system has been extended to multiple near-surface atmospheric variables, including mean, minimum, and maximum 2 m temperature, relative surface humidity, cumulative precipitation, and 10 m wind (speed and direction), the latter two representing particularly challenging targets (Fig. 1). Each variable is downscaled using a dedicated cGAN trained independently to learn the non-linear spatial relationships between coarse-resolution ERA5 predictors and high-resolution VHR-REA_IT targets, while employing a common network architecture and loss function to ensure methodological consistency. This enabled the production of a high-resolution historical dataset covering the period 1990–2024 at daily frequency, with 1990–2000 used for training. Since January 2025, the framework (Fig. 2) has been integrated into an operational chain and used to generate high-resolution fields in near real time, automatically updating the dataset as new ERA5 data become available, with an average latency of approximately six days. All data are distributed in NetCDF format through the CMCC Data Delivery System (https://dds.cmcc.it/) within the FAIR (Fast AI Reanalysis) product, with daily maps accessible via the Dataclime dashboard (https://www.dataclime.com/). Both deterministic and probabilistic configurations of the cGAN framework are presented. Results, evaluated against the dynamically downscaled fields available at the same resolution over the common historical period, show that the proposed approach robustly reproduces spatial patterns (Fig. 3), mean values, and variability across all variables. The probabilistic configuration improves uncertainty representation and shows skill in capturing both mean conditions and extremes. Overall, the framework represents a versatile and robust solution for the generation of high-resolution climate datasets in both historical and operational contexts. Remaining limitations primarily concern the representation of extreme precipitation percentiles in regions characterized by complex orography, which will be the focus of future developments.

Fig. 1 – Wind speed at 10 m for a random day.

Fig. 2 - c-GAN Training Framework

Fig. 3 – Seasonal Analysis. 2-m minimum temperature.