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Statistical downscaling methods (SDMs) are techniques used to downscale and/or bias‐correct climate model results to regional or local scales. The European network VALUE developed a framework to evaluate and inter‐compare SDMs. One of VALUE's experiments is the perfect predictor experiment that uses reanalysis predictors to isolate downscaling skill. Most evaluation papers for SDMs employ simple statistical diagnostics and do not follow a process‐based rationale. Thus, in this paper, a process‐based evaluation has been conducted for the more than 40 participating model output statistics (MOS, mostly bias correction) and perfect prognosis (PP) methods, for temperature and precipitation at 86 weather stations across Europe. The SDMs are analysed following the so‐called "regime‐oriented" technique, focussing on relevant features of the atmospheric circulation at large to local scales. These features comprise the North Atlantic Oscillation, blocking and selected Lamb weather types and at local scales the bora wind and the western Iberian coastal‐low level jet. The representation of the local weather response to the selected features depends strongly on the method class. As expected, MOS is unable to generate process sensitivity when it is not simulated by the predictors (ERA‐Interim). Moreover, MOS often suffers from an inflation effect when a predictor is used for more than one station. The PP performance is very diverse and depends strongly on the implementation. Although conditioned on predictors that typically describe the large‐scale circulation, PP often fails in capturing the process sensitivity correctly. Stochastic generalized linear models supported by well‐chosen predictors show improved skill to represent the sensitivities. [ABSTRACT FROM AUTHOR]

Growing season length (GSL) indices derived from surface air temperature are frequently used in climate monitoring applications. The widely used Expert Team on Climate Change Detection and Indices (ETCCDI) definition aims to give a broadly applicable measure of the GSL that is indicative of the duration of the mild part of the year. In this paper long‐term trends in that index are compared with an alternative measure calculated using a time series decomposition technique (empirical ensemble mode decomposition [EEMD]). It is demonstrated that the ETCCDI index departs from the mild‐season definition as its start and end dates are determined by temperature events operating within the synoptic timescale; this raises the inter‐annual variance of the index. The EEMD‐derived index provides a less noisy and more realistic index of the GSL by filtering out the synoptic‐scale variance and capturing the annual‐cycle and longer timescale variability. Long‐term trends in the GSL are comparable between the two indices, with an average increase in length of around 5 days/decade observed for the period 1965–2016. However, the results using the EEMD index display a more coherent picture of significant trends than has been previously observed. Furthermore, the EEMD‐derived growing season parameters are more closely related to variations in seasonal‐mean hemispheric‐scale atmospheric circulation patterns, with around 57% of the inter‐annual variation in the start of the growing season being connected to the North Atlantic Oscillation and East Atlantic patterns, and around 55% of variation in the end of the growing season being associated with East Atlantic/west Russia‐type patterns. Long‐term trends and inter‐annual variability of the thermal growing season length across Europe are compared by comparing two indices: the ETCCDI definition and a new index calculated using a time series decomposition technique (EEMD). Long‐term trends are comparable between the two indices. However, the EEMD index displays a much more coherent picture of significant trends than has been previously observed, and a much closer relationship to hemispheric modes of atmospheric circulation variability. [ABSTRACT FROM AUTHOR]