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.

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Daily precipitation data from a dense observational network covering Emilia-Romagna, a region of Northern Italy, are described and analysed. Data are available for all stations for the period 1951-2004 and for a selected group of stations located over the Reno hydrological basin for the period 1925-2004. Indices describing seasonal values of mean precipitation and frequency of extreme events are computed starting from daily data and are used to describe the temporal and spatial variability of precipitation over the region.Data referring to the period 1951-2004 are used to describe trends of relevant precipitation indices over the same period in all seasons, and the relation between the variability of these indices and major Euro-Atlantic large-scale circulation indices over winter.Data referring to the period 1925-2004 are used to analyse the decadal and long-term variability over the Reno Basin and its relation with the winter daily discharge of the river. This analysis allows identifying the presence of a clear decadal periodicity in river discharge, strongly related with the decadal variability in both total precipitation and frequency of intense events.

This paper investigates the temporal and spatial variability of the seasonal mean of maximum air temperature in Romania and its links with the large-scale atmospheric circulation. The Romanian data sets are represented by time series at 14 stations. The large-scale parameters are represented by the observed sea-level pressure (SLP) and geopotential height at 500 hPa (Z500). The period analysed was 1922-98 for winter and 1960-98 for all seasons. Before analysis, the original temperature data were tested to detect for inhomogeneity using the standard normal homogeneity test. Empirical orthogonal functions (EOFs) were used to analyse the spatial and temporal variability of the local and large-scale parameters and to eliminate noise from the original data set. The time series associated with the first EOF pattern of the SLP and mean maximum temperature in Romania were analysed from trend and shifts point of view using the Pettitt and Mann-Kendall tests respectively. The covariance map computed using the Z500 and the seasonal mean of maximum temperature in Romania were used as additional methods to identify the large-scale circulation patterns influencing the local variability.Significant increasing trends were found for winter and summer mean maximum temperature in Romania, with upward shifts around 1947 and 1985 respectively. During autumn, a decreasing trend with a downward shift around 1969 was detected. These changes seem to be real, since they are connected to similar changes in the large-scale circulation. So, the intensification of the southwesterly circulation over Europe since 1933 overlapped with the enhancement of westerly circulation after the 1940s could be the reason for the change in winter mean maximum temperature. The slight weakening of the southwesterly circulation during autumn could be one of the reasons for the decrease in the regime of the mean maximum temperature for autumn seasons. Additionally, the covariance map technique reveals the influence of the North Atlantic oscillation in winter, East Atlantic Jet in summer and Scandinavian (or Euroasia-1) circulation pattern in autumn upon mean maximum air temperature.

Optimum statistical downscaling models for three winter precipitation indices in the Emilia-Romagna region, especially related to extreme events, were investigated. For this purpose, the indices referring to the number of events exceeding the long-term 90 percentile of rainy days, simple daily intensity and maximum number of consecutive dry days were calculated as spatial averages over homogeneous sub-regions identified by the cluster analysis. The statistical downscaling model (SDM) based on the canonical correlation analysis (CCA) was used as downscaling procedure. The CCA was also used to understand the large-/regional-scale mechanisms controlling precipitation variability across the analysed area, especially with respect to extreme events. The dynamic (mean sea-level pressure-SLP) and thermodynamic (potential instability-δQ and specific humidity-SH) variables were considered as predictors (either individually or together). The large-scale SLP can be considered a good predictor for all sub-regions in the dry index case and for two sub-regions in the case of the other two indices, showing the importance of dynamical forcing in these cases. Potential instability is the best predictor for the highest mountain region in the case of heavy rainfall frequency, when it can be considered as a single predictor. The combination of dynamic and thermodynamic predictors improves the SDM's skill for all sub-regions in the dry index case and for some sub-regions in the simple daily intensity index case.The selected SDMs are stable in time only in terms of correlation coefficient for all sub-regions for which they are skilful and only for some sub-regions in terms of explained variance. The reasons are linked to the changes in the atmospheric circulation patterns influencing the local rainfall variability in Emilia-Romagna as well as the differences in temporal variability over some sub-regions and sub-intervals. It was concluded that the average skill over an ensemble of the most skilful and stable SDMs for each region/sub-interval gives more consistent results.