Validating and understanding seasonal precipitation and temperature trends in convection-permitting reanalyses over Italy

Reanalyses are widely used to assess climate variability and long-term trends. In recent years, high-resolution regional and convection-permitting reanalyses have provided new opportunities to investigate climate processes and extremes at local scales. However, recent studies have highlighted potential limitations in the temporal consistency of long-term climate signals derived from ERA5 and its dynamical downscalings, raising questions about their suitability for robust trend detection.

This study assesses the temporal consistency of five state-of-the-art convection-permitting regional reanalyses developed for Italy (MORE, CHAPTER, MERIDA-HRES, VHR-REA_IT, and SPHERA), all produced through dynamical downscaling of ERA5 using different numerical models (MOLOCH, WRF, and COSMO). Long-term seasonal trends in both precipitation and temperature are evaluated over the period 1981–2020, depending on the temporal availability of each individual product, against a homogenized observational dataset (UniMI/CNR-ISAC) specifically designed for climate analysis, ensuring temporal consistency at the national scale.

This comparison enables a detailed quantification of uncertainties in trend estimates across different regions and seasons. Inhomogeneities may originate from the driving ERA5 reanalysis or be further introduced during the downscaling process, for instance through the assimilation of local observations (e.g., observational nudging). Similarities and differences among the various reanalyses are examined in the context of the specific architecture and configuration of each product, highlighting how these factors may affect temporal consistency.

By providing a comprehensive assessment of biases and uncertainties in seasonal precipitation and temperature trends over Italy, this work offers guidance to reanalysis users on the reliability of trend estimates for climate studies and impact assessments. It also provides insights for reanalysis developers aimed at improving the temporal consistency of future regional products. Ultimately, the results contribute to strengthening the observational and analytical foundations of climate services, where reliable reanalysis-based products are essential for monitoring and decision-making.