A daily high-resolution gridded climatic data set is presented for Emilia-Romagna, Italy, covering the period 1961–2010. Time series of precipitation and temperatures, from 254 and 60 locations, respectively, were first checked for quality, temporal homogeneity and synchronicity, then interpolated on a grid. For temperature, a daily best-performing detrending procedure was used, followed by the interpolation of regression residuals by means of a modified inverse distance scheme, accounting for orographic barriers. Elevation, urban fraction and topographic position are the geographical proxy parameters used for detrending. The same scheme, without detrending, was used for daily precipitation. All data were spatially interpolated on a high-resolution digital elevation model, and then averaged on a triangulated irregular grid with variable resolution depending on topography. Interpolation determined average errors between 1.0 and 1.5 °C, with higher values for minimum temperatures, in winter and for years prior to 2000. Precipitation is on average underestimated, up to 25% for intense and heavy precipitation in the summer semester. Multiple detrending improves minimum temperature estimation, while the modified distance scheme reduces interpolation errors for temperature and precipitation. The data set is mainly addressed to users and applications requiring time-averaged temperature and precipitation fields. Its main limitations concern precipitation underestimation, winter minimum temperature unexplained variance, unresolved pattern scales, station density and undetected asynchronicities. The data set shows a significant increase in mean annual temperatures all over the region, with trend values up to 0.5 °C decade⁻¹. An average, locally significant, decrease in annual precipitation is also detectable, mostly over the western mountains (−100 mm decade⁻¹), while significant increases are identified in some areas close to the Po River Delta. Local spatial patterns may, however, be susceptible to large errors, especially in low trend areas.

Background: Aedes albopictus, the Asian tiger mosquito, has become a prevalent pest in Italy, causing severe nuisance and posing a threat of transmission of arboviruses introduced by infected travelers. In this study, we investigated the influence of weather parameters on the seasonal population density of Aedes albopictus. Methods: A Bayesian approach was employed to identify the best meteorological predictors of species trend, using the eggs collected monthly from 2010 to 2022 by the Emilia-Romagna regional monitoring network. Results: The findings show that the winter–spring period (January to May) plays a crucial role in the size of the first generation and seasonal development of the species. Conclusions: A temperate winter and a dry and cold March, followed by a rainy and hot spring and a rainy July, seem to favor the seasonal development of Ae. albopictus.

Background: Aedes albopictus, the Asian Tiger Mosquito, has become a prevalent pest in Italy, causing severe nuisance and posing a threat of transmission of arboviruses introduced by infected travellers. In this study, we investigated the influence of weather parameters on the seasonal population density of Aedes albopictus. Methods: A Bayesian approach was employed to identify the best meteorological predictors of species trend, using the eggs collected monthly from 2010 to 2022 by the Emilia-Romagna regional monitoring network. Results: The findings show that the winter-spring period (January to May) plays a crucial role in the size of the first generation and seasonal development of the species. Conclusions: A temperate winter and a dry and cold March, followed by a rainy and hot spring and a rainy July, seem to favour the seasonal development of Ae. albopictus.

Scientific interest is increasingly drawn towards regional meteorological extremes, given their impacts on populations, infrastructure, and ecosystems. These extremes are shaped by complex interactions between internal climate variability and long-term trends. The aim of the present work is to evaluate changes in high-frequency variability and the influence of long-term trends on the frequency of occurrences of extremes, with a focus on surface temperatures over the period from 1962 to 2022 in Emilia-Romagna, a region of Northern Italy. Daily data of 2 m air temperatures averaged over the region are retrieved from ERACLITO, a high-resolution climate analysis. The distributions of daily temperature anomalies show a general broadening in 1992–2022 with respect to 1962–1991. This is true for maximum, minimum, and mean daily surface temperatures, especially during the summer and spring seasons. A significant warming trend of 0.37 °C/decade is detected in annual mean surface temperatures over the period considered. The study is completed with a comparison between the observed frequency of record-breaking annual temperature events, a hypothetical stationary climate distribution and a theoretical derivation that accounts for changes in trends and variability. During the last decade, the theoretical count of extreme events is 1.26, which yields a likelihood of 86% that this is owed to the trend rather than interannual variability. Idealized experiments demonstrate that the expected occurrences of record-breaking events in future decades depend on the warming rate rather than the warming level. Finally, an analysis performed at seasonal level shows that the majority (minority) of record events are occurring in the summer (spring) seasons.