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Simple Summary: Spring in Europe has been trending earlier for almost half a century. Long-distance migrant birds, such as the Willow Warbler and Pied Flycatcher, which breed in Europe, have arrived earlier too. It is broadly accepted that warming springs in temperate regions explain the earlier arrival of migrants. However, migration started weeks earlier and thousands of kilometres away. There must be additional cues elsewhere triggering migration. Meteorologists have developed measures of atmospheric circulation which are related to climate variability in wide regions. One of them is the Southern Oscillation Index, which reflects El Niño/La Niña that cause droughts and floods in the southern hemisphere. Other atmospheric circulation patterns, measured by the North Atlantic Oscillation Index and Indian Ocean Dipole, help predict total rainfall for a whole season in various parts of Africa and Europe. Good rains are associated with plant growth and with insect abundance. Insects provide food for most of these migrants. Therefore, this paper asks the question: "Is the timing of arrival of long-distance migrants in spring related to the climates they experience in the places where they are over the year prior to arrival in Europe?" This paper says the answer is "Yes". Earlier springs in temperate regions since the 1980s, attributed to climate change, are thought to influence the earlier arrival of long-distance migrant passerines. However, this migration was initiated weeks earlier in Africa, where the Southern Oscillation, Indian Ocean Dipole, North Atlantic Oscillation drive climatic variability, and may additionally influence the migrants. Multiple regressions investigated whether 15 indices of climate in Africa and Europe explained the variability in timing of arrival for seven trans-Saharan migrants. Our response variable was Annual Anomaly (AA), derived from standardized mistnetting from 1982–2021 at Bukowo, Polish Baltic Sea. For each species, the best models explained a considerable part of the annual variation in the timing of spring's arrival by two to seven climate variables. For five species, the models included variables related to temperature or precipitation in the Sahel. Similarly, the models included variables related to the North Atlantic Oscillation (for four species), Indian Ocean Dipole (three), and Southern Oscillation (three). All included the Scandinavian Pattern in the previous summer. Our conclusion is that climate variables operating on long-distance migrants in the areas where they are present in the preceding year drive the phenological variation of spring migration. These results have implications for our understanding of carry-over effects. [ABSTRACT FROM AUTHOR]

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]