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The presented work focuses on the investigation of gridded dailyminimum (TN) and maximum (TX) temperature probability density functions(PDFs) with the intent of both characterising a region and detecting extremevalues. The empirical PDFs estimation procedure has been realised using themost recent years of gridded temperature analysis fields available at ARPALombardia, in Northern Italy. The spatial interpolation is based on animplementation of Optimal Interpolation using observations from a densesurface network of automated weather stations. An effort has been made toidentify both the time period and the spatial areas with a stable data densityotherwise the elaboration could be influenced by the unsettled stationdistribution. The PDF used in this study is based on the Gaussiandistribution, nevertheless it is designed to have an asymmetrical (skewed)shape in order to enable distinction between warming and coolingevents. Once properly defined the occurrence of extreme events, it is possibleto straightforwardly deliver to the users the information on a local-scale ina concise way, such as: TX extremely cold/hot or TN extremely cold/hot

An empirical method for estimating probability density functions of gridded daily minimum and maximum temperature