Thermal and Precipitation Conditions during the Thermal Growing Season in Central and Northern Europe

Thermal and Precipitation Conditions during the Thermal Growing Season in Central and Northern Europe | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Thermal and Precipitation Conditions during the Thermal Growing Season in Central and Northern Europe Filip Miś This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7487162/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 05 Dec, 2025 Read the published version in Acta Geophysica → Version 1 posted 5 You are reading this latest preprint version Abstract The study analyzed thermal and precipitation conditions during the thermal growing season (TGS) in Central and Northern Europe over the period 1950–2022. The mean season length was 189 days, with substantial spatial variability ranging from 76 days in northern Scandinavia to 293 days in the southwestern part of the study area. A statistically significant increase in season length was observed over the study period. On average, the season commenced on April 24 and ended on October 30, with its onset and termination shifting towards earlier and later dates, respectively. The mean air temperature during the TGS was 12.1°C, increasing at a rate of 0.13°C/10 years, while the sum of annual temperatures rose on average by 53°C/10 years. The highest rates of change were recorded in the southern part of Central Europe. Precipitation totals during the growing season exhibited pronounced spatial and seasonal variability, with a mean value of 390 mm and a weak decreasing trend (–1.1 mm/10 years). The number of days with precipitation averaged 73, while values of the Hydrothermal Coefficient of Selyaninov (HTC) ranged from 0.5 to over 3.0, with a mean of 1.39, corresponding to optimal conditions for plant development. HTC trends were regionally differentiated but statistically insignificant for the study area as a whole. The results indicate a systematic warming of the growing season and its implications for ecosystem functioning and agricultural production in Europe. Growing season Temperature Europe Precipitation Climate change Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 Figure 11 Figure 12 Introduction Climate change is one of the key drivers shaping contemporary agroclimatic conditions in Europe (IPCC 2021 ). In the context of global warming, the analysis of growing season parameters becomes particularly important, as the growing season plays a fundamental role in determining the productivity of both natural and agricultural ecosystems (Perer 2021; Janni et al. 2024 ). In the regions of Central and Northern Europe, characterized by diverse climates and varying land use patterns, significant changes have been observed in the length of the growing season as well as in the thermal and moisture conditions favorable for plant growth. Previous studies (Lu et al. 2021 ; Kollo et al. 2023 ; Miś and Tomczyk 2025 ) have indicated clear trends towards lengthening of the growing season and increases in its mean temperature. Aalto et al. ( 2022 ) demonstrated that, during the period 1950–2019, the thermal growing season in Northern Europe began on average 15 days earlier, and its duration increased by 23 days. Additionally, the sum of growing degree days rose by 287°C, with the most pronounced changes recorded in coastal areas and in the eastern part of the analyzed region. Research conducted in Poland (Kejna and Rudzki 2021 ; Szyga-Pluta 2022 ), Scandinavia (Ketzler et al. 2020 ; Lohtander and Räisänen 2024 ), and the Baltic States (Lakson et al. 2019 ; Kalvāns 2023) confirms shifts in thermal boundaries as well as the occurrence of regional differences in the intensity of these changes. In the scientific literature, the growing season is often analyzed based on the sum of effective temperatures calculated above a specific thermal threshold (commonly 5°C). This approach is widely applied in climatology and agroclimatology, as it enables a quantitative assessment of the thermal potential of the season and facilitates comparisons between regions and years (Yin et al. 2019 ; Grigorieva 2020 ). The sum of effective temperatures also allows for the forecasting of plant phenology and yield potential; however, it does not fully account for hydrological factors or thermal stresses such as frost events. For this reason, it is increasingly combined with precipitation analyses and hydrothermal indices, such as the Hydrothermal Coefficient of Selyaninov (HTC), which considers both water availability and thermal conditions during the plant growth period (Selyaninov 1930 ). Due to its simplicity and interpretability, the HTC is widely used in climate and agrometeorological research (Chmist-Sikorska and Struzik 2022 ; Rybashlykova 2025 ). An example is the study by Evarte-Bundere and Evarts-Bunders ( 2012 ), who applied the HTC to assess the influence of hydrothermal conditions on the distribution of alien Tilia species in Latvia. They demonstrated that deviations of the HTC from its optimal value correlated with an increased incidence of frost damage in the analyzed taxa. Although thermal factors largely determine the length and intensity of the growing season, precipitation conditions are equally important, as they govern water availability for plants. The occurrence of atmospheric or soil drought during critical developmental stages can limit plant growth, even under favorable thermal conditions (Fernández et al. 2014 ; Zhang and Hu 2018 ). Moreover, the increasing frequency and intensity of extreme weather events, such as heatwaves, heavy rainfall, or frost during transitional periods, poses a serious threat to the stability and quality of agricultural production (Schmitt et al. 2012; Li et al. 2022 ; Nidzgorska-Lencewicz et al. 2024 ). The total precipitation and the number of days with rainfall during the growing season have a significant influence on vegetation development (Ru et al. 2018 ; Eck et al. 2020 ; Grusson et al. 2021 ). Despite numerous studies, there remains a lack of comprehensive, long-term analyses combining thermal and precipitation conditions across the entire area of Central and Northern Europe. Such research is essential for advancing knowledge on the impacts of climate change on agriculture and ecosystems, as well as for developing effective adaptation strategies. In the face of increasing climate-related threats, the development and implementation of adaptive measures such as the selection of more resilient crop varieties, optimization of cultivation timing, and water resource management are becoming crucial for mitigating the negative impacts of climate change and maintaining the stability of agricultural production (Challinor et al. 2014 ; Change 2016 ; Anderson et al. 2020 ). The aim of this study was to conduct a comprehensive analysis of changes in thermal and precipitation conditions during the thermal growing season in Central and Northern Europe over the period 1950–2022, with particular emphasis on season length, cumulative temperatures, atmospheric precipitation, the number of days with precipitation, and values of the Hydrothermal Coefficient (HTC), as well as to assess their trends and potential implications for agriculture and ecosystem functioning. Materials and Methods The study utilized mean daily air temperature (T mean ) and mean daily total atmospheric precipitation (P sum ) values for the period 1950–2022, obtained from the European Climate Assessment and Dataset (ECA&D) reanalysis (Haylock et al. 2008 ). The reanalysis data were acquired in NetCDF format as gridded datasets with a spatial resolution of 0.25° × 0.25° for the area between 5° and 40° E and 47.5° and 70° N. This area was defined as Central and Northern Europe. For a detailed comparison of thermal and precipitation conditions, six reference points were established, evenly distributed across the study area. These points were located in the following cities: Kyiv (Ukraine, 50°22′ N, 30°37′ E), Copenhagen (Denmark, 55°22′ N, 12°52′ E), Munich (Germany, 48°07′ N, 11°37′ E), Rovaniemi (Finland, 66°22′ N, 25°37′ E), Tallinn (Estonia, 59°22′ N, 24°52′ E), and Warsaw (Poland, 52°07′ N, 21°07′ E) (Fig. 1). Figure 1 Study area location The predominant portion of the studied area is comprised of agricultural lands and forested regions. According to the Corine Land Cover database, 71% of the investigated territory is covered by agricultural and forested land (the database excludes Belarus, Russia, and Ukraine). The delineated region is characterized by a high proportion of land utilized for agricultural production, making it particularly suitable for research on agroclimatic conditions in the context of intensifying climate change. The growing season is defined as the period within a year during which the mean daily air temperature exceeds the threshold value necessary for plant growth and development, most commonly set at 5°C (Kożuchowski 2011 ). In addition to suitable thermal conditions, appropriate hydrological conditions particularly soil water availability essential for the proper functioning of plant physiological processes are critical for the initiation and progression of the growing season (Kramer 1983 ). In the scientific literature, the growing season is determined using various approaches, differing both in the selected thermal threshold and in the methodology applied to identify its onset and cessation. One frequently used definition refers to the so-called frost-free period, in which the start of the growing season is taken as the last spring day with frost (T min 0°C), and the end is defined as the first autumn day with frost (Wypych et al. 2017 ; Strong and McCabe 2017 ; Ning et al. 2017 ; Zhu and Yan 2023 ). The growing season may also be identified based on phenological observations (Fu et al. 2014 ; Piao et al. 2019 ; Iler et al. 2021 ). For this purpose, in addition to traditional observation methods, satellite-derived data are increasingly utilized (Høgda et al. 2013 ; Karlsen et al. 2021 , 2022 ). Both approaches have limitations: in the case of phenological observations, these primarily relate to the spatial availability of data, whereas for satellite-derived datasets, the main constraints concern their temporal continuity and accessibility. An alternative approach to determining the growing season involves the use of air temperature threshold values, referred to as the thermal growing season. Frich et al. ( 2002 ) defined the beginning of the growing season as the first day of a 5-day sequence with T mean >5°C, and the end as the first day of a 5-day sequence with T mean < 5°C. In contrast, Carter ( 1998 ) extended the terminal sequence to 10 consecutive days. In the present study, the growing season was determined using a method analogous to that proposed by Linderholm (2008) and Dong et al. ( 2012 ). In these studies, the onset of the growing season is defined as the first day of a 6-day sequence with T mean >5°C following the last spring frost (T mean < 0°C), while its termination is defined as the last day before a 10-day sequence with T mean < 5°C. Walther and Linderholm ( 2006 ) noted that omitting frost-related criteria may lead to inaccurate results. In the present study, we adopted the methodology proposed by Miś and Tomczyk ( 2025 ), who defined the growing season as commencing with a sequence of six consecutive days with T mean >5°C following the last spring frost (T mean < 0°C) and terminating with a sequence of six consecutive days with T mean < 5°C following the first autumn frost (T mean < 0°C). This approach standardizes the sequence length for both the onset and cessation of the thermal growing season. Using this method, the onset and termination dates of the thermal growing season were determined. These calculations enabled the estimation of mean daily air temperature and the cumulative temperature sum during the growing season. Furthermore, thermal anomalies associated with exceptionally extreme seasons were identified, based on the highest and lowest seasonal mean values calculated for the entire study area. Subsequently, the total precipitation during the thermal growing season and the number of days with measurable precipitation (P sum >0.0 mm) were computed. For both parameters, extreme values were determined to facilitate the identification of exceptionally dry or wet seasons. For all analyzed parameters, temporal trends were assessed, and their statistical significance was evaluated using the non-parametric Mann–Kendall test (p < 0.05). The magnitude of trends was estimated using Sen’s non-parametric linear regression method (Salmi et al. 2002 ). Additionally, based on daily air temperature values and daily precipitation totals, the hydrothermal coefficient of Selyaninov (HTC) was calculated for each growing season, following the methodology proposed by Selyaninov ( 1930 ). This coefficient is expressed by the formula: $$\:HTC=\frac{\sum\:{P}_{>{10}^{\circ\:}C}}{0.1\cdot\:\sum\:{T}_{>{10}^{\circ\:}C}}$$ where: ΣP > 10°C – the sum of daily precipitation totals during the thermal growing season, i.e., on days when the mean daily air temperature exceeded 10°C; ΣT > 10°C – the sum of mean daily air temperatures during the thermal growing season, i.e., on days when the mean daily air temperature exceeded 10°C. For the interpretation of HTC values, a ten-class classification scale, widely applied in the relevant literature, was used: HTC < 0.4 – extremely dry; 0.4 ≤ HTC < 0.8 – very dry; 0.8 ≤ HTC < 1.1 – dry; 1.1 ≤ HTC < 1.4 – quite dry; 1.4 ≤ HTC < 1.7 – optimal; 1.7 ≤ HTC < 2.1 – quite humid; 2.1 ≤ HTC < 2.6 – humid; 2.6 ≤ HTC < 3.0 – very humid; HTC ≥ 3.0 – extremely humid. All statistical computations and the preparation of maps and graphs were carried out using the R programming language. Results Between 1950 and 2022, the mean length of the thermal growing season (TGS) across the study area was 189 days, exhibiting substantial spatial variability, ranging from 76 days in northern Scandinavia to 293 days in the southwestern part of the study area (the Netherlands) (Fig. 2a). In the 21st century, 69% of the seasons were longer than the average for the period 1950–2000. Of the ten longest seasons, four occurred within the last five years (2019–2022), while the remaining seasons took place in 1961, 1990, 2000, 2008, 2010, and 2011 thus, seven occurred in the 21st century. The TGS showed a tendency to lengthen towards the south and west. The mean onset date of the growing season during the study period was 24 April (Fig. 2b). The earliest onsets were observed in the southwestern part of the study area (the Netherlands, western Germany, northern France), occurring between 19 and 28 February. The latest onsets occurred in northern Scandinavia and the Scandinavian Mountains, between 19 and 28 June. Over the past two decades, only one season (2003) began later than the multi-year mean. The average termination date of the TGS was 30 October (Fig. 2c). The earliest terminations were recorded in northern Scandinavia and central Norway (17–26 September), whereas the latest occurred in the Netherlands, Belgium, Denmark, and western Germany (6–15 December). In the 21st century, 65% of the seasons ended later than the mean for the period 1950–2000. Figure 2 Mean length of thermal growing season ( a ), mean date of start ( b ), mean date of end ( c ) The spatial pattern of mean air temperature during the TGS exhibits substantial variability across the study area (Fig. 3a). The mean air temperature for this period was 12.1°C. The lowest values were recorded in the northern part of Scandinavia, where the mean temperature ranged between 3–5°C, whereas the highest values occurred in eastern Ukraine, in the southeastern portion of the study area, reaching 17–19°C. Among the analyzed years, 1962 was the coldest (Fig. 3b), with a mean TGS air temperature of 10.8°C. The lowest values again occurred in northern Scandinavia (1–3°C), while the highest were observed in southeastern Ukraine (16–17°C). In contrast, 2018 was the warmest year of the study period (Fig. 3c), with a mean TGS air temperature of 13.7°C. The coolest areas during that year were located in the mountainous zone of southern Norway (7–8°C), whereas the warmest areas encompassed the southern portion of the study area, including eastern Ukraine and northern Hungary, where mean temperatures reached 18–20°C. The trend in air temperature during the thermal growing season was unequivocally positive across the entire study area (Fig. 3d). The average rate of temperature increase was 0.13°C/10 years. The lowest trend value (0.0°C/10 years) was observed in central Sweden, while the highest values (> 0.3°C/10 years) occurred in the southern part of the study area, particularly in Slovakia, Austria, and southern Germany. Notably, for more than 80% of the study area, the detected changes were statistically significant. Figure 3 Mean air temperature ( a ), coldest year in the study period, 1962 ( b ), warmest year in the study period, 2018 ( c ), rate of change (red dots – statistically significant changes) ( d ) The analysis of reference stations revealed substantial variability in mean air temperature during the TGS (Fig. 4). Mean air temperatures across the study period ranged from 11.2°C in Rovaniemi to 15.0°C in Kyiv. The remaining stations recorded the following values: 12.0°C in Copenhagen and Tallinn, 12.9°C in Munich, and 13.6°C in Warsaw. The lowest mean air temperature for a single growing season was recorded in 1987 in Rovaniemi, at 9.2°C, whereas the highest was observed in 1979 in Kyiv, with a mean seasonal temperature of 16.8°C. In the 21st century, the vast majority of growing seasons exhibited values exceeding the long-term mean. The proportion of seasons with above-average air temperature ranged from 55% in Copenhagen to 82% in Munich and as high as 86% in Tallinn. The rate of change in mean air temperature during the growing season varied among stations. The lowest rates were observed in Copenhagen and Tallinn (0.09°C/10 years), while the highest occurred in Munich (0.21°C/10 years). The remaining stations recorded rates of 0.10°C/10 years in Warsaw, 0.12°C/10 years in Kyiv, and 0.15°C/10 years in Rovaniemi. The statistical significance of these changes was confirmed for all stations except Copenhagen. Figure 4 Variability and rate of change in air temperature during the thermal growing season The mean cumulative air temperature during the TGS was 2341°C (Fig. 5a). This value exhibited substantial spatial variability, ranging from 454°C in southern Norway to 4059°C in the southernmost part of the study area, encompassing northern Hungary. A systematic increase in cumulative air temperature was observed with progression towards the south. The lowest mean seasonal value of this indicator occurred in 1976 (Fig. 5b), amounting to 2052°C. The minimum value was recorded in northern Scandinavia (359°C), while the maximum was observed in northern Hungary (3891°C). In contrast, the highest mean seasonal cumulative air temperature was recorded in 2018 (Fig. 5c), when the mean for the entire study area reached 2786°C an increase of more than 400°C relative to the long-term average. That year, the lowest values were again found in northern Scandinavia (623°C), and the highest once more in northern Hungary (4626°C). All analyzed grid points exhibited a positive trend in cumulative air temperature over the study period (Fig. 5d). The average rate of increase was 53°C/10 years, with values ranging from 9°C to 131°C/10 years. Statistically significant changes were identified for 99% of the study area. Figure 5 Cumulative air temperature ( a ), lowest total in the study period, 1976 ( b ), highest total in the study period, 2018 ( c ), rate of change (red dots – statistically significant changes) ( d ) The analysis of cumulative air temperature during the TGS revealed a clear upward trend at all reference stations. Mean values of this indicator for the period 1950–2022 ranged from 1671°C in Rovaniemi to 3339°C in Kyiv (Fig. 6). The remaining locations recorded the following values: 2332°C in Tallinn, 3072°C in Munich, 3099°C in Warsaw, and 3101°C in Copenhagen. The lowest seasonal value was observed in Rovaniemi in 1977 (1273°C), while the highest was recorded in Kyiv in 2010 (4000°C). In the 21st century, the proportion of seasons warmer than the long-term mean ranged from 86% in Tallinn to 95% in both Kyiv and Munich. All locations exhibited a statistically significant increase in cumulative air temperature during the TGS. The rate of change ranged from 51.1°C/10 years in Rovaniemi to 83.6°C/10 years in Munich. Correlation coefficients for these trends were statistically significant for all measurement sites. Figure 6 Cumulative air temperature and its trends during the thermal growing season The mean total precipitation during the TGS in the period 1950–2022 was 390 mm (Fig. 7a). The highest precipitation totals were recorded along the western coast of Norway, where they locally exceeded 1600 mm. In contrast, the lowest seasonal totals occurred in northern Scandinavia, where precipitation sums were below 200 mm. The spatial distribution indicates that higher precipitation totals are concentrated in mountainous areas and in proximity to large water bodies. The driest season of the entire study period was 1959 (Fig. 7b), with a mean precipitation total of 297 mm. The highest totals that year were again observed along the western coast of Norway (approximately 1200–1400 mm), while the lowest occurred in central and northern Sweden, where local values did not exceed 100 mm. Conversely, the wettest season was recorded in 1998 (Fig. 7c), with a mean precipitation total of 487 mm for the entire study area. In that year, the minimum values were recorded in the far north of Norway (slightly above 100 mm), whereas the maximum occurred along the western coast of Norway and in the Alpine region, where local totals exceeded 1000 mm and in some places surpassed 1400 mm. Trend analysis revealed substantial spatial variability in the rate of change in precipitation totals (Fig. 7d). The mean trend for the entire area was − 1.1 mm/10 years, with the strongest decreases observed in southern Norway, locally exceeding 50 mm/10 years. The largest increases occurred in central Poland, Ukraine, and eastern Russia, where they surpassed 20 mm/10 years. Only about 10% of the study area exhibited statistically significant changes, indicating a relatively low consistency in the long-term direction of precipitation trends. Figure 7 Total precipitation ( a ), lowest total in the study period, 1959 ( b ), highest total in the study period, 1998 ( c ), rate of change (red dots – statistically significant changes) ( d ) The mean total precipitation during the TGS in the study period ranged from 285 mm in Rovaniemi to 736 mm in Munich (Fig. 8). The corresponding values for the remaining locations were: 365 mm in Kyiv, 385 mm in Tallinn, 386 mm in Warsaw, and 421 mm in Copenhagen. The highest seasonal precipitation total was recorded in Munich in 2002, amounting to 1042 mm, while the lowest was observed in Rovaniemi in 1969, with only 131 mm. The analysis indicated an increase in precipitation totals at all reference stations. However, the rate of change varied considerably by location, from 2.3 mm/10 years in Kyiv to 14.6 mm/10 years in Copenhagen. The high uncertainty associated with the trend estimates reflects substantial interannual variability in precipitation, which hinders an unambiguous interpretation of long-term changes. Among the stations analyzed, only Copenhagen and Munich exhibited statistically significant trends, suggesting that the observed increases in precipitation at these sites are the most robust and temporally consistent. Figure 8 Total precipitation and its trends during the thermal growing season The number of days with precipitation during the thermal growing season (TGS) showed considerable spatial variation, ranging from 37 days in northern Scandinavia to 142 days along the western coast of Norway and in the Netherlands (Fig. 9a). The mean number of precipitation days across the study area was 73 days. Higher precipitation frequency was observed in mountainous regions and coastal zones, whereas the lowest values occurred in the continental interior. The lowest mean number of precipitation days in the TGS was recorded in 1955 (Fig. 9b), when the average for the entire study area was 58 days. Particularly low values occurred in central and northern Scandinavia, where the number of precipitation days locally dropped below 23. In contrast, the highest values that year were recorded in central Germany, exceeding 130 days. The year 1974 was characterised by the highest mean number of precipitation days during the growing season, reaching 89 days (Fig. 9c). The fewest precipitation days in 1974 occurred in northwestern Russia (slightly above 40 days), whereas the highest numbers were recorded in western Germany and the Netherlands, locally exceeding 190 days. The analysis of trends in the number of precipitation days revealed pronounced spatial variability (Fig. 9d). An increase in precipitation days dominated in the central and eastern parts of the study area, while a decrease occurred mainly in its northern and western parts. The average trend value was − 0.14 days/10 years. The most pronounced decline was observed in Denmark and southern Sweden, reaching − 4.1 days/10 years, whereas the largest increase occurred in central Poland, where the number of precipitation days rose by 4.2 days/10 years. Statistical significance of the detected changes was confirmed for only about 10% of the study area, primarily in regions where the changes were the greatest. Figure 9 Number of days with precipitation ( a ), lowest number of precipitation days during the study period in 1955 ( b ), highest number of precipitation days during the study period in 1974 ( c ), and trend in the number of precipitation days (red dots indicate statistically significant changes) ( d ) The number of days with precipitation during the growing season (TGS) at the reference sites exhibited pronounced spatial variability (Fig. 10). In an average season, these values ranged from 56 days in Rovaniemi (northern Finland) to 112 days in Munich (southern Germany). At other locations, the number of precipitation days was as follows: 65 days in Kyiv, 74 days in Warsaw, 76 days in Tallinn, and 98 days in Copenhagen. It is noteworthy that despite Warsaw having a slightly higher mean precipitation sum, the number of precipitation days was lower than in Tallinn, where the total precipitation was marginally less. The lowest recorded number of precipitation days was observed in Rovaniemi in 1968 (35 days), whereas the highest was documented in Copenhagen in 2020 (164 days). Trend analysis of precipitation days revealed a positive rate of change across all analyzed stations except Tallinn, where no significant changes were detected (0.0 days/10 years). The highest rate of increase was identified in Copenhagen, amounting to 2.1 days/10 years. Despite the predominantly positive direction, trend estimates were characterized by considerable uncertainty. In many instances, the standard errors exceeded the trend estimates themselves, indicating that actual changes could potentially have been in the opposite, negative direction. The absence of statistically significant correlation coefficients for the trend across all investigated stations further corroborates the uncertainty regarding a definitive direction of change in the number of precipitation days over the studied period. Figure 10 Temporal variation in the number of precipitation days and the rate of change during the thermal growing season The values of the HTC index over the analyzed multi-year period exhibited a pronounced dependence on topography and proximity to large water bodies. Higher values were recorded in areas situated at greater altitudes above sea level and near seas, whereas lower values occurred in lowland regions distant from water bodies, where a continental climate predominated. The mean HTC value for the studied area was 1.39, corresponding to optimal conditions for plant development (Fig. 11a). The lowest values were observed in central Ukraine and central Poland, where HTC ranged between 0.5 and 0.7, indicating very dry conditions. Conversely, the highest values were recorded in the Alps, the Carpathians, and particularly along the southwestern coast of Norway, where average values exceeded 3.0 and local maxima reached up to 5.2, indicative of extremely humid conditions. The driest season within the analyzed period was 1959 (Fig. 11b). The average HTC for the entire area that year was 0.98, indicating dry conditions. Index values ranged from 0.25 in central Sweden to 5.8 in northern Norway. In 1959, as much as 64% of the study area experienced conditions classified as extremely dry, very dry, or dry. In contrast, the highest seasonal HTC value was recorded in 1998 (Fig. 11c), when the mean value reached 1.85, corresponding to relatively humid conditions. The range of values was substantial, from 0 (extremely dry) in a small area of south-central Norway to 6.4 (extremely humid) in southern Norway. Analysis of temporal changes in HTC values revealed spatial heterogeneity (Fig. 11d). The southern and western parts of the study area exhibited a decreasing trend (negative), while northern and eastern regions showed an increasing trend (positive). The average trend value for the entire area was 0.00/10 years, indicating no significant overall change. Trend values varied from − 0.12/10 years in western Poland to 0.20/10 years in southern Norway. The statistical significance of the trend coefficient was confirmed over 14% of the study area, indicating limited, though locally pronounced changes. Figure 11 Average HTC index value ( a ), lowest HTC value during the multi-year period, year 1959 ( b ), highest HTC index value during the multi-year period, year 1998 ( c ), and rate of change (red dots indicate statistically significant changes) ( d ) Analysis of the HTC index at selected reference points revealed significant spatial variability of this indicator over the analyzed multi-year period (Fig. 12). Mean values ranged from 0.96 in Kyiv, indicating dry conditions, to 2.01 in Munich, corresponding to humid conditions. At the remaining stations, HTC values were as follows: 1.12 in Copenhagen and Warsaw (quite dry conditions), 1.39 in Tallinn (optimal conditions), and 1.43 in Rovaniemi (also optimal conditions). The greatest inter-seasonal variability of the HTC index was observed in Rovaniemi, which exhibited both the lowest and highest values among all reference stations. In 1969, HTC reached 0.32, classified as extremely dry conditions, whereas in 1998 it peaked at 3.10, classified as extremely humid conditions. Changes in HTC values at the analyzed points were generally stable. Rates of change ranged from − 0.005/10 years in Munich to 0.03/10 years in Rovaniemi. In all cases, the estimates were characterized by considerable statistical uncertainty, suggesting that the direction of change may not be unequivocal. No statistically significant trends were detected at any of the analyzed stations. Figure 12 Temporal variation of HTC index values and the rate of change during the thermal growing season Summary and discussion Thermal and precipitation conditions in Central and Northern Europe during the thermal growing season (TGS) were analyzed for the multi-decadal period 1950–2022. The analysis revealed significant spatial variability in TGS length, with a mean duration of 189 days and a pronounced upward trend. The length of the TGS ranged from 76 days in northern Scandinavia to 293 days in southwestern Europe. These results are consistent with previous studies on the growing season in Europe (Linderholm et al. 2008 ; Ruosteenoja et al. 2011 ; Aalto et al. 2022 ). On average, the season commenced on 24 April and ended on 30 October, with both the onset and termination dates exhibiting temporal shifts towards earlier and later dates, respectively. Similar trends were reported by Szyga-Pluta et al. ( 2023 ), who demonstrated that in Poland, the growing season is expected to begin substantially earlier and end later. The authors estimated that, between 1966 and 2020, the onset of the growing season in Poland advanced by an average of approximately 3 days, while its termination was delayed by around 2 days. During the study period, the mean air temperature throughout the thermal growing season (TGS) was 12.1°C, exhibiting pronounced spatial variability, ranging from 3–5°C in northern Scandinavia to 17–19°C in eastern Ukraine. The coldest year was 1962 (10.8°C), while the highest mean seasonal temperature was recorded in 2018 (13.7°C). Trend analysis revealed a clear and statistically significant increase in air temperature across most of the study area, with a mean rate of change of 0.13°C/10 years. The strongest warming was observed in the southern part of Central Europe. Among all reference stations analyzed, the highest positive trend was recorded in Munich, located in the southern portion of the study domain, where the rate of temperature increase reached 0.21°C/10 years. Comparable findings have been reported in numerous studies addressing the thermal conditions of the growing season (Bąk and Łabędzki 2014 ; Waldau et al. 2018; Xue et al. 2020 ). Similar results were presented by Tomczyk and Szyga-Pluta ( 2019 ), who, in their analysis of thermal and precipitation variability during the growing season in Poland, found that the mean air temperature in Warsaw ranged between 13.6 and 14.0°C. The present study corroborates this value, indicating that in Warsaw, the mean growing season temperature was 13.6°C. The rate of temperature increase reported by Tomczyk and Szyga-Pluta ( 2019 ) ranged from 0.15 to 0.19°C/10 years, whereas in the present analysis it amounted to 0.10°C/10 years. Cumulative air temperatures during the thermal growing season (TGS) exhibited a clear and statistically significant upward trend across Central–Northern Europe in the period 1950–2022. The mean cumulative temperature for the entire study area during the TGS was 2341°C, with pronounced spatial variability ranging from 454°C in southern Norway to over 4000°C in the southernmost parts of the region, including, among others, northern Hungary. These findings are consistent with earlier studies addressing changes in the thermal conditions of the growing season in Europe (Solantie 2004 ; Verbai et al. 2014 ; Kanapickas et al. 2022 ). In the study by Wypych et al. ( 2017 ) on the spatial variability of cumulative growing season air temperatures in Poland, the mean value amounted to 3150°C, with the highest values recorded in the western part of the country. Comparable results were obtained in the present analysis, where cumulative temperatures for Poland ranged from 2800 to 3200°C, with maxima exceeding 3200°C in the western regions. Similar outcomes were also reported by Graczyk and Kundzewicz ( 2016 ) and Tomczyk and Szyga-Pluta ( 2019 ). The lowest mean cumulative air temperature during the thermal growing season (TGS) in the analyzed period occurred in 1976 (2052°C), whereas the highest was recorded in 2018 (2786°C). In the long-term perspective, the average rate of increase in this index amounted to 53°C/10 years, although considerable regional variability was observed, ranging from 9°C to 131°C/10 years. Statistically significant positive trends were recorded at all reference stations, with the highest rate observed in Munich (83.6°C/10 years). Comparable rates of change were reported by Spinoni et al. ( 2015 ), who, analyzing the period 1951–2010, identified an increase in cumulative TGS air temperatures of 25°C/10 years in Scandinavia, 38°C/10 years in the Baltic States, and 48°C/10 years in Germany and Denmark. The results of the present study confirm these observations, with changes in Scandinavia ranging from 20–40°C/10 years, in the Baltic States from 40–60°C/10 years, and in Germany and Denmark exceeding 60°C/10 years. The observed increase clearly indicates a systematic warming of the thermal conditions of the growing season in Europe, with significant implications for both ecosystem functioning and agricultural production. Many plant species are highly dependent on cumulative air temperatures, which determine their phenological development as well as productivity. As air temperatures rise and the growing season lengthens in Central and Northern Europe, a northward expansion of thermophilous species such as maize ( Zea mays ), sunflower ( Helianthus annuus ), grapevine ( Vitis vinifera ), and soybean ( Glycine max ) can be anticipated, as confirmed by the findings of Desclaux and Roumet ( 1996 ), Dahmardeh ( 2012 ), Khoufi et al. ( 2013 ), and Ortega-Farias and Riveros-Burgos ( 2019 ). At the same time, a gradual decline of species sensitive to heat stress, such as Norway spruce ( Picea abies ) and European beech ( Fagus sylvatica ), is expected, as indicated by Sykes et al. ( 1996 ). Analysis of cumulative precipitation totals revealed pronounced spatial and interannual variability. The mean precipitation total for the entire study area was 390 mm, with the lowest values observed in northern Scandinavia (below 200 mm) and the highest along the western coast of Norway, where local totals exceeded 1600 mm. The driest growing season occurred in 1959, with a regional mean of 297 mm, while the wettest was recorded in 1998, with a total of 487 mm. The precipitation totals for Finland obtained in the present study, ranging from 200 to 400 mm during the thermal growing season (TGS), are consistent with the results reported by Ylhäisi et al. ( 2010 ). These authors, analyzing data from regional climate simulations for the period 1961–1990, demonstrated that the mean seasonal precipitation total in Finland (for April–September) ranged from approximately 220 mm in the north to 420 mm in the southwest, values that correspond to the duration and characteristics of the TGS. These findings fall within the range obtained in the present analysis and confirm the presence of relatively low, yet regionally variable, precipitation totals during the TGS. On a regional scale, the results can also be compared with those of Tomczyk and Szyga-Pluta ( 2019 ), who reported that the average growing season precipitation total in Poland was 430 mm, with the lowest totals in central Poland and the highest in the south. These findings are consistent with the present study, in which the long-term mean precipitation total ranged from 200–400 mm in central Poland to over 600 mm in the south. The conducted analysis revealed a mean decrease in cumulative precipitation of − 1.1 mm/10 years across the entire region, with local values reaching as low as − 50 mm/10 years in southern Norway. In contrast, the largest positive trends were observed in central Poland, Ukraine, and eastern Russia, locally reaching 20 mm/10 years. The statistical significance of these changes was low, with only approximately 10% of the analyzed area exhibiting significant trends, highlighting the high degree of interannual precipitation variability. Positive precipitation trends were recorded at all reference stations, although the rates varied from 2.3 mm/10 years in Kyiv to 14.6 mm/10 years in Copenhagen. Statistically significant trends were found only in Copenhagen and Munich, suggesting greater stability and reliability of precipitation changes in these locations. Similar spatial variability in precipitation trends across Europe has also been reported by Van den Besselaar et al. ( 2013 ), Caloiero et al. ( 2018 ), and Zeder and Fischer ( 2020 ). The number of days with precipitation during the TGS period exhibited pronounced spatial and temporal variability. The mean number of days with precipitation across the entire study area was 73 days, ranging from 37 days in northern Scandinavia to 142 days along the western coast of Norway and in the Netherlands. Numerous regional studies conducted in Central and Northern Europe corroborate these findings (Bengtsson and Rana 2013; Brázdil et al. 2021 ; Repel et al. 2021 ). In the wettest season (1974), the local number of days with precipitation exceeded 190 days, whereas in the driest season (1955), it fell below 23 days in northern Scandinavia. Over the analyzed multi-year period, the mean trend amounted to -0.14 days/10 years, with regional variations ranging from decreases of 4.1 days/10 years in Denmark and southern Sweden to increases of 4.2 days/10 years in central Poland. These findings are supported by Skowera et al. ( 2014 ), who, in their analysis of trends in the number of days with precipitation from April to September, reported an increase of 4.14 days/10 years for the Poznań area in central-western Poland. At reference stations, the number of days with precipitation ranged from 56 days in Rovaniemi to 112 days in Munich, with the highest positive trend observed in Copenhagen (2.1 days/10 years). Despite the predominance of positive trends, their statistical significance was low, and the high uncertainty of the estimates indicates no clear directional change in the number of precipitation days over the study period. The analysis of the hydrothermal coefficient (HTC) during the TGS period revealed significant spatial variability, primarily driven by topography and proximity to water bodies. The mean HTC value across the entire study area was 1.39, corresponding to optimal conditions for plant growth. The lowest values (0.5–0.7) were recorded in central Poland and Ukraine, indicating very dry conditions, whereas the highest values (above 3.0, with a maximum of 6.4) occurred in mountainous and coastal regions such as the Alps and southwestern Norway. Based on the data presented by Chmist-Sikorska and Struzik ( 2022 ), meteorological droughts in Poland from April to September occur relatively frequently, with their frequency and intensity exhibiting regional variability, being most pronounced in the central part of the country. According to the analyzed HTC indicator, these droughts corresponded to values within the range of 0.4–0.8 (very dry conditions) and below 0.4 (extremely dry conditions). The results of the present analysis are consistent with the study by Evarte-Bundere and Evarts-Bunders ( 2012 ), who assessed the HTC in Latvia and reported mean values ranging from 1.3 to 2.0 during the growing season, indicating optimal conditions. The driest season occurred in 1959 (mean HTC: 0.98), whereas the wettest season was in 1998 (mean HTC: 1.85). Over the analyzed multi-year period, no significant changes were observed at the scale of the entire study area (0.00/10 years); however, local trends included both positive and negative variations, ranging from 0.12/10 years in western Poland to 0.20/10 years in southern Norway. Similar patterns were reported by Chmist-Sikorska et al. (2022) and Samborski ( 2024 ), who analyzed HTC for the Zamość region (southeastern Poland) and found a decreasing trend of 0.13/10 years. At reference stations, HTC values ranged from 0.96 in Kyiv (dry conditions) to 2.01 in Munich (humid conditions), with the highest seasonal variability observed in Rovaniemi, where HTC fluctuated between 0.32 and 3.10. Despite regional variability, none of the observed trends reached statistical significance, indicating the absence of clear and persistent changes in hydrothermal conditions over the study period. The HTC indicator accounts for both precipitation totals and air temperature patterns, providing a more comprehensive measure of hydrothermal conditions than indices based solely on one of these factors. Knowledge of HTC values and trends in a given region enables informed adaptation of crop and tree species selection to changing climatic conditions. In areas exhibiting an increasing HTC trend, the introduction of species more sensitive to water deficit, requiring higher moisture and temperature, may be feasible. Conversely, in regions with a decreasing HTC trend, it is advisable to limit the cultivation of water-demanding species in favor of more drought-tolerant and thermally resilient plants. Declarations Author contributions FM was involved in conceptualisation, data curation, formal analysis, investigation, methodology, project administration, resources, software, validation, visualisation and writing. Funding No funding was received for conducting this study. Data availability The data presented in this study are openly available at https://surfobs.climate.copernicus.eu/dataaccess/access_eobs.php#datafiles Conflict of interest The authors assert that they do not have any known conflicting financial interests or personal affiliations that might have appeared to impact the research presented in this paper. References Aalto J, Pirinen P, Kauppi PE, Rantanen M, Lussana C, Lyytikäinen-Saarenmaa P, Gregow H (2022) High-resolution analysis of observed thermal growing season variability over northern Europe. Clim Dyn 58:1477–1493. https://doi.org/10.1007/s00382-021-05970-y Anderson R, Bayer PE, Edwards D (2020) Climate change and the need for agricultural adaptation. Curr Opin Plant Biol 56:197–202. https://doi.org/10.1016/j.pbi.2019.12.006 Bąk B, Łabędzki L (2014) Thermal conditions in Bydgoszcz Region in growing seasons of 2011–2050 in view of expected climate change. J Water Land Dev. https://doi.org/10.1515/jwld-2014-0026 Bengtsson L, Rana A (2014) Long‐term change of daily and multi‐daily precipitation in southern Sweden. Hydrol Process 28:2897–2911. https://doi.org/10.1002/hyp.9774 Brázdil R, Zahradníček P, Dobrovolný P, Štěpánek P, Trnka M (2021) Observed changes in precipitation during recent warming: The Czech Republic, 1961–2019. Int J Climatol 41:3881–3902. https://doi.org/10.1002/joc.7048 Caloiero T, Caloiero P, Frustaci F (2018) Long‐term precipitation trend analysis in Europe and in the Mediterranean basin. Water Environ J 32:433–445. https://doi.org/10.1111/wej.12346 Carter TR (1998) Changes in the thermal growing season in Nordic countries during the past century and prospects for the future. Agric For Meteorol 7:161–179. https://doi.org/10.23986/afsci.72857 Challinor AJ, Watson J, Lobell DB, Howden SM, Smith DR, Chhetri N (2014) A meta-analysis of crop yield under climate change and adaptation. Nat Clim Change 4:287–291. https://doi.org/10.1038/nclimate2153 Change C (2016) Agriculture and food security. In: The State of Food and Agriculture. FAO, Rome Chmist-Sikorska J, Struzik P (2022) Agricultural drought assessment on the base of Hydro-thermal Coefficient of Selyaninov in Poland. Ital J Agrometeorol 1:3–12. https://doi.org/10.36253/ijam-1530 Dahmardeh M (2012) Effects of sowing date on the growth and yield of maize cultivars (Zea mays L.) and the growth temperature requirements. Afr J Biotechnol 11:12450–12453. https://doi.org/10.5897/AJB12.201 Desclaux D, Roumet P (1996) Impact of drought stress on the phenology of two soybean (Glycine max L. Merr) cultivars. Field Crops Res 46:61–70. https://doi.org/10.1016/0378-4290(95)00086-0 Dong M, Jiang Y, Zheng C, Zhang D (2012) Trends in the thermal growing season throughout the Tibetan Plateau during 1960–2009. Agric For Meteorol 166:201–206. https://doi.org/10.1016/j.agrformet.2012.07.013 Eck MA, Murray AR, Ward AR, Konrad CE (2020) Influence of growing season temperature and precipitation anomalies on crop yield in the southeastern United States. Agric For Meteorol 291:108053. https://doi.org/10.1016/j.agrformet.2020.108053 Evarte-Bundere G, Evarts-Bunders P (2012) Using of the hydrothermal coefficient (HTC) for interpretation of distribution of non-native tree species in Latvia on example of cultivated species of genus Tilia. Acta Biol Univ Daugavp 12:135–148 Fernández ME, Gyenge JE, Varela S, de Urquiza M (2014) Effects of the time of drought occurrence within the growing season on growth and survival of Pinus ponderosa seedlings. Trees 28:745–756. https://doi.org/10.1007/s00468-014-0986-1 Frich PALV, Alexander LV, Della-Marta P, Gleason B, Haylock M, Tank AK, Peterson T (2002) Observed coherent changes in climatic extremes during the second half of the twentieth century. Clim Res 19(3): 193-212. https://doi.org/10.3354/cr019193 Fu YH, Piao S, Op de Beeck M, Cong N, Zhao H, Zhang Y, Menzel A, Janssens IA (2014) Recent spring phenology shifts in western Central Europe based on multiscale observations. Glob Ecol Biogeogr 23:1255–1263. https://doi.org/10.1111/geb.12210 Graczyk D, Kundzewicz ZW (2016) Changes of temperature-related agroclimatic indices in Poland. Theor Appl Climatol 124:401–410. https://doi.org/10.1007/s00704-015-1429-7 Grigorieva E (2020) Evaluating the sensitivity of growing degree days as an agro-climatic indicator of the climate change impact: A case study of the Russian far East. Atmosphere 11:404. https://doi.org/10.3390/atmos11040404 Grusson Y, Wesström I, Joel A (2021) Impact of climate change on Swedish agriculture: Growing season rain deficit and irrigation need. Agric Water Manag 251:106858. https://doi.org/10.1016/j.agwat.2021.106858 Haylock MR, Hofstra N, Klein Tank AMG, Klok EJ, Jones PD, New M (2008) A European daily high-resolution gridded data set of surface temperature and precipitation for 1950–2006. J Geophys Res 113:D20119. https://doi.org/10.1029/2008JD010201 Høgda KA, Tømmervik H, Karlsen SR (2013) Trends in the start of the growing season in Fennoscandia 1982–2011. Remote Sens 5:4304–4318. https://doi.org/10.3390/rs5094304 Iler AM, CaraDonna PJ, Forrest JR, Post E (2021) Demographic consequences of phenological shifts in response to climate change. Annu Rev Ecol Evol Syst 52:221–245. https://doi.org/10.1146/annurev-ecolsys-011921-032939 IPCC (2021) Climate change 2021: the physical science basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge University Press, Cambridge Janni M, Maestri E, Gullì M, Marmiroli M, Marmiroli N (2024) Plant responses to climate change, how global warming may impact on food security: a critical review. Front Plant Sci 14:1297569. https://doi.org/10.3389/fpls.2023.1297569 Kalvāns A, Kalvāne G, Zandersons V, Gaile D, Briede A (2023) Recent seasonally contrasting and persistent warming trends in Latvia. Theor Appl Climatol 154:125–139. https://doi.org/10.1007/s00704-023-04540-y Kanapickas A, Vagusevičienė I, Juknys R, Sujetovienė G (2022) Effects of climatic and cultivar changes on winter wheat phenology in central Lithuania. Int J Biometeorol 66:2009–2020. https://doi.org/10.1007/s00484-022-02336-9 Karlsen SR, Stendardi L, Tømmervik H, Nilsen L, Arntzen I, Cooper EJ (2021) Time-series of cloud-free Sentinel-2 NDVI data used in mapping the onset of growth of central Spitsbergen, Svalbard. Remote Sens 13:3031. https://doi.org/10.3390/rs13153031 Karlsen SR, Elvebakk A, Tømmervik H, Belda S, Stendardi L (2022) Changes in onset of vegetation growth on Svalbard, 2000–2020. Remote Sens 14:6346. https://doi.org/10.3390/rs14246346 Kejna M, Rudzki M (2021) Spatial diversity of air temperature changes in Poland in 1961–2018. Theor Appl Climatol 143:1361–1379. https://doi.org/10.3390/atmos13081232 Ketzler G, Römer W, Beylich AA (2020) The climate of Norway. In: Landscapes and Landforms of Norway. Springer, Cham, pp 7–29. https://doi.org/10.1007/978-3-030-52563-7_2 Khoufi S, Khamassi K, Teixeira da Silva JA, Aoun N, Rezgui S, Ben Jeddi F (2013) Assessment of diversity of phenologically and morphologically related traits among adapted populations of sunflower (Helianthus annuus L.). Helia 36:29–40. https://doi.org/10.2298/HEL1358029K Kollo J, Metslaid S, Padari A, Hordo M, Kangur A, Noe SM (2023) Trends in thermal growing season length from years 1955–2020 – A case study in hemiboreal forest in Estonia. Boreal Environ Res 28:169–180 Kożuchowski K (2011) Klimat Polski: nowe spojrzenie. Wydawnictwo Naukowe PWN, Warszawa (in Polish) Kramer PJ (1983) Problems in water relations of plants and cells. Int Rev Cytol 85:253–286. https://doi.org/10.1016/S0074-7696(08)62375-X Lakson M, Post P, Sepp M (2019) The impact of atmospheric circulation on air temperature rise in Estonia. Front Earth Sci 7:131. https://doi.org/10.3389/feart.2019.00131 Li L, Hao Y, Zheng Z, Wang W, Biederman JA, Wang Y, Xu Z (2022) Heavy rainfall in peak growing season had larger effects on soil nitrogen flux and pool than in the late season in a semiarid grassland. Agric Ecosyst Environ 326:107785. https://doi.org/10.1016/j.agee.2021.107785 Linderholm HW, Walther A, Chen D (2008) Twentieth-century trends in the thermal growing season in the Greater Baltic Area. Clim Change 87:405–419. https://doi.org/10.1007/s10584-007-9327-3 Lohtander A, Räisänen DJ (2024) Changes in diurnal temperature range in Finland between 1961–1990 and 1991–2020 Lu M, Sun H, Yan D, Xue J, Yi S, Gui D, Zhang W (2021) Projections of thermal growing season indices over China under global warming of 1.5 °C and 2.0 °C. Sci Total Environ 781:146774. https://doi.org/10.1016/j.scitotenv.2021.146774 Miś F, Tomczyk AM (2025) Spatial and temporal differentiation of the thermal growing season in central and northern Europe. Theor Appl Climatol 156:1–14. https://doi.org/10.1007/s00704-025-05382-6 Nidzgorska-Lencewicz J, Mąkosza A, Koźmiński C, Michalska B (2024) Potential risk of frost in the growing season in Poland. Agriculture 14:501. https://doi.org/10.3390/agriculture14030501 Ning X, Liu G, Zhang L, Qin X, Zhou S, Qin Y (2017) The spatio-temporal variations of frost-free period in China from 1951 to 2012. J Geogr Sci 27:23–42. https://doi.org/10.1007/s11442-017-1362-z Ortega-Farias S, Riveros-Burgos C (2019) Modeling phenology of four grapevine cultivars (Vitis vinifera L.) in Mediterranean climate conditions. Sci Hortic 250:38–44. https://doi.org/10.1016/j.scienta.2019.02.025 Perer J (2023) Effect of global warming on agricultural productivity. Int J Agric Piao S, Liu Q, Chen A, Janssens IA, Fu Y, Dai J, Zhu X (2019) Plant phenology and global climate change: Current progresses and challenges. Glob Change Biol 25:1922–1940. https://doi.org/10.1111/gcb.14619 Repel A, Zeleňáková M, Jothiprakash V, Hlavatá H, Blišťan P, Gargar I, Purcz P (2021) Long-term analysis of precipitation in Slovakia. Water 13:952. https://doi.org/10.3390/w13070952 Ru J, Zhou Y, Hui D, Zheng M, Wan S (2018) Shifts of growing‐season precipitation peaks decrease soil respiration in a semiarid grassland. Glob Change Biol 24:1001–1011. https://doi.org/10.1111/gcb.13941 Ruosteenoja K, Räisänen J, Pirinen P (2011) Projected changes in thermal seasons and the growing season in Finland. Int J Climatol 31:1473–1487. https://doi.org/10.1002/joc.2171 Rybashlykova LP (2025) Variability in hydrothermal coefficient (HTC) and productivity of pasture ecosystems of Tersko-Kuma lowland, Russia. J Agrometeorol 27:236–238. https://doi.org/10.54386/jam.v27i2.2653 Salmi T, Määttä A, Anttila P, Ruoho-Airola T, Amnell T (2002) Detecting trends of annual values of atmospheric pollutants by the Mann-Kendall test and Sen's slope estimates – the Excel template application MAKESENS. Publ Air Qual 31, Finnish Meteorological Institute, Helsinki Samborski AS (2024) Agroclimatic characterization of Zamosc, Poland using hydrothermal coefficient (HTC). J Agrometeorol 26:473–476. https://doi.org/10.54386/jam.v26i4.2655 Schmitt J, Offermann F, Söder M, Frühauf C, Finger R (2022) Extreme weather events cause significant crop yield losses at the farm level in German agriculture. Food Policy 112:102359. https://doi.org/10.1016/j.foodpol.2022.102359 Selyaninov GT (1930) Method of determination of agricultural climate characteristic. Trans Agric Meteorol 21 Skowera B, Kopcińska J, Kopeć B (2014) Changes in thermal and precipitation conditions in Poland in 1971–2010. Ann Warsaw Univ Life Sci-SGGW Land Reclam 46. https://doi.org/10.2478/sggw-2014-0013 Solantie R (2004) Daytime temperature sum – a new thermal variable describing growing season characteristics and explaining evapotranspiration. Boreal Environ Res 9:319 Spinoni J, Naumann G, Vogt J, Barbosa P. (2015) European drought climatologies and trends based on a multi-indicator approach. Global and Planetary Change 127: 50-57. https://doi.org/10.1016/j.gloplacha.2015.01.012 Strong C, McCabe GJ (2017) Observed variations in U.S. frost timing linked to atmospheric circulation patterns. Nat Commun 8:15307. https://doi.org/10.1038/ncomms15307 Sykes MT, Prentice IC, Cramer W (1996) A bioclimatic model for the potential distributions of north European tree species under present and future climates. J Biogeogr:203–233 Szyga-Pluta K (2022) Assessment of changing agroclimatic conditions in Poland based on selected indicators. Atmosphere 13:1232 Szyga-Pluta K, Tomczyk AM, Piniewski M, Eini MR (2023) Past and future changes in the start, end, and duration of the growing season in Poland. Acta Geophys 71:3041–3055. https://doi.org/10.1007/s11600-023-01117-1 Tomczyk AM, Szyga-Pluta K (2019) Variability of thermal and precipitation conditions in the growing season in Poland in the years 1966–2015. Theor Appl Climatol 135:1517–1530. https://doi.org/10.1007/s00704-018-2450-4 Van den Besselaar EJM, Klein Tank AMG, Buishand TA (2013) Trends in European precipitation extremes over 1951–2010. Int J Climatol 33. https://doi.org/10.1002/joc.3619 Verbai Z, Lazar I, Kalmár F (2014) Heating degree day in Hungary. Environ Eng Manag J 13:6 Waldau T, Chmielewski FM (2018) Spatial and temporal changes of spring temperature, thermal growing season and spring phenology in Germany 1951–2015. https://doi.org/10.1127/metz/2018/0923 Walther A, Linderholm HW (2006) A comparison of growing season indices for the Greater Baltic Area. Int J Biometeorol 51:107–118. https://doi.org/10.1007/s00484-006-0048-5 Wypych A, Sulikowska A, Ustrnul Z, et al. (2017) Variability of growing degree days in Poland in response to ongoing climate changes in Europe. Int J Biometeorol 61:49–59. https://doi.org/10.1007/s00484-016-1190-3 Xue F, Jiang Y, Wang M, Dong M, Ding X, Yang X, Kang M (2020) Temperature and thermal growing season variations along elevational gradients on a sub-alpine, temperate China. Theor Appl Climatol 140:15–24. https://doi.org/10.1007/s00704-019-03067-5 Yin Y, Deng H, Wu S (2019) Spatial-temporal variations in the thermal growing degree-days and season under climate warming in China during 1960–2011. Int J Biometeorol 63:649–658. https://doi.org/10.1007/s00484-017-1417-y Ylhäisi JS, Tietäväinen H, Peltonen-Sainio P, Venäläinen A, Eklund J, Räisänen J, Jylhä K (2010) Growing season precipitation in Finland under recent and projected climate. Nat Hazards Earth Syst Sci 10:1563–1574. https://doi.org/10.5194/nhess-10-1563-2010 Zeder J, Fischer EM (2020) Observed extreme precipitation trends and scaling in Central Europe. Weather Clim Extremes 29:100266. https://doi.org/10.1016/j.wace.2020.100266 Zhang Q, Hu Z (2018) Assessment of drought during corn growing season in Northeast China. Theor Appl Climatol 133:1315–1321. https://doi.org/10.1007/s00704-018-2469-6 Zhu L, Yan X (2023) Change and attribution of frost days and frost‐free periods in China. Int J Climatol 43:7935–7953. https://doi.org/10.1002/joc.8310 Cite Share Download PDF Status: Published Journal Publication published 05 Dec, 2025 Read the published version in Acta Geophysica → Version 1 posted Reviewers agreed at journal 04 Sep, 2025 Reviewers invited by journal 04 Sep, 2025 Editor invited by journal 02 Sep, 2025 Editor assigned by journal 02 Sep, 2025 First submitted to journal 31 Aug, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7487162","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":510171383,"identity":"2c9bc264-2549-414e-b8d4-7855bf9524c1","order_by":0,"name":"Filip Miś","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAuklEQVRIiWNgGAWjYBAC9mbmBoYEBhsIjweI+Qhp4TnMCNKShtDCRlDLAaAWBobDpGhhZ2zd8ODPeXndGQmMD962MeQR1sLM2HYjse224bYbCcyGc9sYiglqsQdrabjNCNTCJs3bxpDYRpQtCX/O2QO1sP8mQQvbgUSQLczEa0lsS07eduZhs+SccxKE/cLDf/jYzR9/7Gy3HU8++OFNmU0ePyEtSAAcQRIJJOiAAjK0jIJRMApGwXAHAP+gPsGxKicXAAAAAElFTkSuQmCC","orcid":"https://orcid.org/0009-0006-1462-7316","institution":"Adam Mickiewicz University: Uniwersytet im Adama Mickiewicza w Poznaniu","correspondingAuthor":true,"prefix":"","firstName":"Filip","middleName":"","lastName":"Miś","suffix":""}],"badges":[],"createdAt":"2025-08-29 09:36:15","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7487162/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7487162/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s11600-025-01730-2","type":"published","date":"2025-12-05T15:57:58+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":91031322,"identity":"956e8396-e858-4712-872d-512913bec794","added_by":"auto","created_at":"2025-09-11 00:32:03","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":19342263,"visible":true,"origin":"","legend":"\u003cp\u003eStudy area location\u003c/p\u003e","description":"","filename":"Fig.1.png","url":"https://assets-eu.researchsquare.com/files/rs-7487162/v1/a0a82cd7fa706d669894fdd0.png"},{"id":91030767,"identity":"c41365c5-a0f0-49d9-80dd-952a4a04811c","added_by":"auto","created_at":"2025-09-11 00:08:02","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":3142378,"visible":true,"origin":"","legend":"\u003cp\u003eMean length of thermal growing season (\u003cstrong\u003ea\u003c/strong\u003e), mean date of start (\u003cstrong\u003eb\u003c/strong\u003e), mean date of end (\u003cstrong\u003ec\u003c/strong\u003e)\u003c/p\u003e","description":"","filename":"Fig.2.png","url":"https://assets-eu.researchsquare.com/files/rs-7487162/v1/0d81e6ffaa7748eaa285a2c2.png"},{"id":91031008,"identity":"f9ab59ec-406b-448b-8a55-b3e61d8bc31e","added_by":"auto","created_at":"2025-09-11 00:16:03","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":4042541,"visible":true,"origin":"","legend":"\u003cp\u003eMean air temperature (\u003cstrong\u003ea\u003c/strong\u003e), coldest year in the study period, 1962 (\u003cstrong\u003eb\u003c/strong\u003e), warmest year in the study period, 2018 (\u003cstrong\u003ec\u003c/strong\u003e), rate of change (red dots – statistically significant changes) (\u003cstrong\u003ed\u003c/strong\u003e)\u003c/p\u003e","description":"","filename":"Fig.3.png","url":"https://assets-eu.researchsquare.com/files/rs-7487162/v1/3af6254206740e1b06e808f2.png"},{"id":91031005,"identity":"da07a6e7-c48a-4917-8229-96855921c60c","added_by":"auto","created_at":"2025-09-11 00:16:02","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":3706047,"visible":true,"origin":"","legend":"\u003cp\u003eVariability and rate of change in air temperature during the thermal growing season\u003c/p\u003e","description":"","filename":"Fig.4.png","url":"https://assets-eu.researchsquare.com/files/rs-7487162/v1/795d7a67f0bece050c2cfd58.png"},{"id":91030770,"identity":"d6826876-f6f3-4413-bbd7-a80f440da7e7","added_by":"auto","created_at":"2025-09-11 00:08:02","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":4445459,"visible":true,"origin":"","legend":"\u003cp\u003eCumulative air temperature (\u003cstrong\u003ea\u003c/strong\u003e), lowest total in the study period, 1976 (\u003cstrong\u003eb\u003c/strong\u003e), highest total in the study period, 2018 (\u003cstrong\u003ec\u003c/strong\u003e), rate of change (red dots – statistically significant changes) (\u003cstrong\u003ed\u003c/strong\u003e)\u003c/p\u003e","description":"","filename":"Fig.5.png","url":"https://assets-eu.researchsquare.com/files/rs-7487162/v1/5d63b8634b493303bbd7f21b.png"},{"id":91030786,"identity":"bf8f363e-f1cf-4a05-a06a-6c81b653d094","added_by":"auto","created_at":"2025-09-11 00:08:03","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":4277648,"visible":true,"origin":"","legend":"\u003cp\u003eCumulative air temperature and its trends during the thermal growing season\u003c/p\u003e","description":"","filename":"Fig.6.png","url":"https://assets-eu.researchsquare.com/files/rs-7487162/v1/244dcfd93b3395663cfa313a.png"},{"id":91030777,"identity":"ce9865ca-853a-4573-a656-31bcef50f2db","added_by":"auto","created_at":"2025-09-11 00:08:03","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":3455436,"visible":true,"origin":"","legend":"\u003cp\u003eTotal precipitation (\u003cstrong\u003ea\u003c/strong\u003e), lowest total in the study period, 1959 (\u003cstrong\u003eb\u003c/strong\u003e), highest total in the study period, 1998 (\u003cstrong\u003ec\u003c/strong\u003e), rate of change (red dots – statistically significant changes) (\u003cstrong\u003ed\u003c/strong\u003e)\u003c/p\u003e","description":"","filename":"Fig.7.png","url":"https://assets-eu.researchsquare.com/files/rs-7487162/v1/32d49adeb149bd0d7926c099.png"},{"id":91030780,"identity":"1ac58dd5-0c69-46e2-888f-c89fd6053fc5","added_by":"auto","created_at":"2025-09-11 00:08:03","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":2794647,"visible":true,"origin":"","legend":"\u003cp\u003eTotal precipitation and its trends during the thermal growing season\u003c/p\u003e","description":"","filename":"Fig.8.png","url":"https://assets-eu.researchsquare.com/files/rs-7487162/v1/777aadaa38aeee6730904278.png"},{"id":91030771,"identity":"305bfacf-5958-421c-8da0-38ae20f45964","added_by":"auto","created_at":"2025-09-11 00:08:03","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":3699513,"visible":true,"origin":"","legend":"\u003cp\u003eNumber of days with precipitation (\u003cstrong\u003ea\u003c/strong\u003e), lowest number of precipitation days during the study period in 1955 (\u003cstrong\u003eb\u003c/strong\u003e), highest number of precipitation days during the study period in 1974 (\u003cstrong\u003ec\u003c/strong\u003e), and trend in the number of precipitation days (red dots indicate statistically significant changes) (\u003cstrong\u003ed\u003c/strong\u003e)\u003c/p\u003e","description":"","filename":"Fig.9.png","url":"https://assets-eu.researchsquare.com/files/rs-7487162/v1/e93e312cd40bfaee327faf5d.png"},{"id":91031013,"identity":"e734e5fe-39f3-4439-8425-e62c180fe438","added_by":"auto","created_at":"2025-09-11 00:16:03","extension":"png","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":3244088,"visible":true,"origin":"","legend":"\u003cp\u003eTemporal variation in the number of precipitation days and the rate of change during the thermal growing season\u003c/p\u003e","description":"","filename":"Fig.10.png","url":"https://assets-eu.researchsquare.com/files/rs-7487162/v1/013cd9ab34bf94aedf62e4a4.png"},{"id":91030790,"identity":"48dfc9b3-c9b9-4230-a67f-7dd33d7af19c","added_by":"auto","created_at":"2025-09-11 00:08:03","extension":"png","order_by":11,"title":"Figure 11","display":"","copyAsset":false,"role":"figure","size":3658419,"visible":true,"origin":"","legend":"\u003cp\u003eAverage HTC index value (\u003cstrong\u003ea\u003c/strong\u003e), lowest HTC value during the multi-year period, year 1959 (\u003cstrong\u003eb\u003c/strong\u003e), highest HTC index value during the multi-year period, year 1998 (\u003cstrong\u003ec\u003c/strong\u003e), and rate of change (red dots indicate statistically significant changes) (\u003cstrong\u003ed\u003c/strong\u003e)\u003c/p\u003e","description":"","filename":"Fig.11.png","url":"https://assets-eu.researchsquare.com/files/rs-7487162/v1/f65db58221d3686f2e21e072.png"},{"id":91031017,"identity":"e4dfe502-8e0e-4a31-9914-6498c8399d56","added_by":"auto","created_at":"2025-09-11 00:16:03","extension":"png","order_by":12,"title":"Figure 12","display":"","copyAsset":false,"role":"figure","size":3366454,"visible":true,"origin":"","legend":"\u003cp\u003eTemporal variation of HTC index values and the rate of change during the thermal growing season\u003c/p\u003e","description":"","filename":"Fig.12.png","url":"https://assets-eu.researchsquare.com/files/rs-7487162/v1/6438d6d48c326f7a31060d07.png"}],"financialInterests":"","formattedTitle":"Thermal and Precipitation Conditions during the Thermal Growing Season in Central and Northern Europe","fulltext":[{"header":"Introduction","content":"\u003cp\u003eClimate change is one of the key drivers shaping contemporary agroclimatic conditions in Europe (IPCC \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). In the context of global warming, the analysis of growing season parameters becomes particularly important, as the growing season plays a fundamental role in determining the productivity of both natural and agricultural ecosystems (Perer 2021; Janni et al. \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). In the regions of Central and Northern Europe, characterized by diverse climates and varying land use patterns, significant changes have been observed in the length of the growing season as well as in the thermal and moisture conditions favorable for plant growth. Previous studies (Lu et al. \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Kollo et al. \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Miś and Tomczyk \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2025\u003c/span\u003e) have indicated clear trends towards lengthening of the growing season and increases in its mean temperature. Aalto et al. (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) demonstrated that, during the period 1950\u0026ndash;2019, the thermal growing season in Northern Europe began on average 15 days earlier, and its duration increased by 23 days. Additionally, the sum of growing degree days rose by 287\u0026deg;C, with the most pronounced changes recorded in coastal areas and in the eastern part of the analyzed region. Research conducted in Poland (Kejna and Rudzki \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Szyga-Pluta \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), Scandinavia (Ketzler et al. \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Lohtander and R\u0026auml;is\u0026auml;nen \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), and the Baltic States (Lakson et al. \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Kalvāns 2023) confirms shifts in thermal boundaries as well as the occurrence of regional differences in the intensity of these changes.\u003c/p\u003e\u003cp\u003eIn the scientific literature, the growing season is often analyzed based on the sum of effective temperatures calculated above a specific thermal threshold (commonly 5\u0026deg;C). This approach is widely applied in climatology and agroclimatology, as it enables a quantitative assessment of the thermal potential of the season and facilitates comparisons between regions and years (Yin et al. \u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Grigorieva \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). The sum of effective temperatures also allows for the forecasting of plant phenology and yield potential; however, it does not fully account for hydrological factors or thermal stresses such as frost events. For this reason, it is increasingly combined with precipitation analyses and hydrothermal indices, such as the Hydrothermal Coefficient of Selyaninov (HTC), which considers both water availability and thermal conditions during the plant growth period (Selyaninov \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e1930\u003c/span\u003e). Due to its simplicity and interpretability, the HTC is widely used in climate and agrometeorological research (Chmist-Sikorska and Struzik \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Rybashlykova \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). An example is the study by Evarte-Bundere and Evarts-Bunders (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2012\u003c/span\u003e), who applied the HTC to assess the influence of hydrothermal conditions on the distribution of alien \u003cem\u003eTilia\u003c/em\u003e species in Latvia. They demonstrated that deviations of the HTC from its optimal value correlated with an increased incidence of frost damage in the analyzed taxa.\u003c/p\u003e\u003cp\u003eAlthough thermal factors largely determine the length and intensity of the growing season, precipitation conditions are equally important, as they govern water availability for plants. The occurrence of atmospheric or soil drought during critical developmental stages can limit plant growth, even under favorable thermal conditions (Fern\u0026aacute;ndez et al. \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Zhang and Hu \u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Moreover, the increasing frequency and intensity of extreme weather events, such as heatwaves, heavy rainfall, or frost during transitional periods, poses a serious threat to the stability and quality of agricultural production (Schmitt et al. 2012; Li et al. \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Nidzgorska-Lencewicz et al. \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). The total precipitation and the number of days with rainfall during the growing season have a significant influence on vegetation development (Ru et al. \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Eck et al. \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Grusson et al. \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Despite numerous studies, there remains a lack of comprehensive, long-term analyses combining thermal and precipitation conditions across the entire area of Central and Northern Europe. Such research is essential for advancing knowledge on the impacts of climate change on agriculture and ecosystems, as well as for developing effective adaptation strategies. In the face of increasing climate-related threats, the development and implementation of adaptive measures such as the selection of more resilient crop varieties, optimization of cultivation timing, and water resource management are becoming crucial for mitigating the negative impacts of climate change and maintaining the stability of agricultural production (Challinor et al. \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Change \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Anderson et al. \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe aim of this study was to conduct a comprehensive analysis of changes in thermal and precipitation conditions during the thermal growing season in Central and Northern Europe over the period 1950\u0026ndash;2022, with particular emphasis on season length, cumulative temperatures, atmospheric precipitation, the number of days with precipitation, and values of the Hydrothermal Coefficient (HTC), as well as to assess their trends and potential implications for agriculture and ecosystem functioning.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cp\u003eThe study utilized mean daily air temperature (T\u003csub\u003emean\u003c/sub\u003e) and mean daily total atmospheric precipitation (P\u003csub\u003esum\u003c/sub\u003e) values for the period 1950\u0026ndash;2022, obtained from the European Climate Assessment and Dataset (ECA\u0026amp;D) reanalysis (Haylock et al. \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). The reanalysis data were acquired in NetCDF format as gridded datasets with a spatial resolution of 0.25\u0026deg; \u0026times; 0.25\u0026deg; for the area between 5\u0026deg; and 40\u0026deg; E and 47.5\u0026deg; and 70\u0026deg; N. This area was defined as Central and Northern Europe. For a detailed comparison of thermal and precipitation conditions, six reference points were established, evenly distributed across the study area. These points were located in the following cities: Kyiv (Ukraine, 50\u0026deg;22\u0026prime; N, 30\u0026deg;37\u0026prime; E), Copenhagen (Denmark, 55\u0026deg;22\u0026prime; N, 12\u0026deg;52\u0026prime; E), Munich (Germany, 48\u0026deg;07\u0026prime; N, 11\u0026deg;37\u0026prime; E), Rovaniemi (Finland, 66\u0026deg;22\u0026prime; N, 25\u0026deg;37\u0026prime; E), Tallinn (Estonia, 59\u0026deg;22\u0026prime; N, 24\u0026deg;52\u0026prime; E), and Warsaw (Poland, 52\u0026deg;07\u0026prime; N, 21\u0026deg;07\u0026prime; E) (Fig.\u0026nbsp;1).\u003c/p\u003e\u003cp\u003e\u003cb\u003eFigure\u0026nbsp;1\u003c/b\u003e Study area location\u003c/p\u003e\u003cp\u003eThe predominant portion of the studied area is comprised of agricultural lands and forested regions. According to the Corine Land Cover database, 71% of the investigated territory is covered by agricultural and forested land (the database excludes Belarus, Russia, and Ukraine). The delineated region is characterized by a high proportion of land utilized for agricultural production, making it particularly suitable for research on agroclimatic conditions in the context of intensifying climate change. The growing season is defined as the period within a year during which the mean daily air temperature exceeds the threshold value necessary for plant growth and development, most commonly set at 5\u0026deg;C (Kożuchowski \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). In addition to suitable thermal conditions, appropriate hydrological conditions particularly soil water availability essential for the proper functioning of plant physiological processes are critical for the initiation and progression of the growing season (Kramer \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e1983\u003c/span\u003e). In the scientific literature, the growing season is determined using various approaches, differing both in the selected thermal threshold and in the methodology applied to identify its onset and cessation. One frequently used definition refers to the so-called frost-free period, in which the start of the growing season is taken as the last spring day with frost (T\u003csub\u003emin\u003c/sub\u003e \u0026lt; 0\u0026deg;C and T\u003csub\u003emax\u003c/sub\u003e \u0026gt;0\u0026deg;C), and the end is defined as the first autumn day with frost (Wypych et al. \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Strong and McCabe \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Ning et al. \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Zhu and Yan \u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). The growing season may also be identified based on phenological observations (Fu et al. \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Piao et al. \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Iler et al. \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). For this purpose, in addition to traditional observation methods, satellite-derived data are increasingly utilized (H\u0026oslash;gda et al. \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Karlsen et al. \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2021\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Both approaches have limitations: in the case of phenological observations, these primarily relate to the spatial availability of data, whereas for satellite-derived datasets, the main constraints concern their temporal continuity and accessibility. An alternative approach to determining the growing season involves the use of air temperature threshold values, referred to as the thermal growing season. Frich et al. (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2002\u003c/span\u003e) defined the beginning of the growing season as the first day of a 5-day sequence with T\u003csub\u003emean\u003c/sub\u003e \u0026gt;5\u0026deg;C, and the end as the first day of a 5-day sequence with T\u003csub\u003emean\u003c/sub\u003e \u0026lt; 5\u0026deg;C. In contrast, Carter (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e1998\u003c/span\u003e) extended the terminal sequence to 10 consecutive days. In the present study, the growing season was determined using a method analogous to that proposed by Linderholm (2008) and Dong et al. (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). In these studies, the onset of the growing season is defined as the first day of a 6-day sequence with T\u003csub\u003emean\u003c/sub\u003e \u0026gt;5\u0026deg;C following the last spring frost (T\u003csub\u003emean\u003c/sub\u003e \u0026lt; 0\u0026deg;C), while its termination is defined as the last day before a 10-day sequence with T\u003csub\u003emean\u003c/sub\u003e \u0026lt; 5\u0026deg;C. Walther and Linderholm (\u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e2006\u003c/span\u003e) noted that omitting frost-related criteria may lead to inaccurate results.\u003c/p\u003e\u003cp\u003eIn the present study, we adopted the methodology proposed by Miś and Tomczyk (\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2025\u003c/span\u003e), who defined the growing season as commencing with a sequence of six consecutive days with T\u003csub\u003emean\u003c/sub\u003e \u0026gt;5\u0026deg;C following the last spring frost (T\u003csub\u003emean\u003c/sub\u003e \u0026lt; 0\u0026deg;C) and terminating with a sequence of six consecutive days with T\u003csub\u003emean\u003c/sub\u003e \u0026lt; 5\u0026deg;C following the first autumn frost (T\u003csub\u003emean\u003c/sub\u003e \u0026lt; 0\u0026deg;C). This approach standardizes the sequence length for both the onset and cessation of the thermal growing season.\u003c/p\u003e\u003cp\u003eUsing this method, the onset and termination dates of the thermal growing season were determined. These calculations enabled the estimation of mean daily air temperature and the cumulative temperature sum during the growing season. Furthermore, thermal anomalies associated with exceptionally extreme seasons were identified, based on the highest and lowest seasonal mean values calculated for the entire study area. Subsequently, the total precipitation during the thermal growing season and the number of days with measurable precipitation (P\u003csub\u003esum\u003c/sub\u003e \u0026gt;0.0 mm) were computed. For both parameters, extreme values were determined to facilitate the identification of exceptionally dry or wet seasons. For all analyzed parameters, temporal trends were assessed, and their statistical significance was evaluated using the non-parametric Mann\u0026ndash;Kendall test (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). The magnitude of trends was estimated using Sen\u0026rsquo;s non-parametric linear regression method (Salmi et al. \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2002\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eAdditionally, based on daily air temperature values and daily precipitation totals, the hydrothermal coefficient of Selyaninov (HTC) was calculated for each growing season, following the methodology proposed by Selyaninov (\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e1930\u003c/span\u003e). This coefficient is expressed by the formula:\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$$\\:HTC=\\frac{\\sum\\:{P}_{\u0026gt;{10}^{\\circ\\:}C}}{0.1\\cdot\\:\\sum\\:{T}_{\u0026gt;{10}^{\\circ\\:}C}}$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003ewhere:\u003c/p\u003e\u003cp\u003eΣP\u0026thinsp;\u0026gt;\u0026thinsp;10\u0026deg;C \u0026ndash; the sum of daily precipitation totals during the thermal growing season, i.e., on days when the mean daily air temperature exceeded 10\u0026deg;C;\u003c/p\u003e\u003cp\u003eΣT\u0026thinsp;\u0026gt;\u0026thinsp;10\u0026deg;C \u0026ndash; the sum of mean daily air temperatures during the thermal growing season, i.e., on days when the mean daily air temperature exceeded 10\u0026deg;C.\u003c/p\u003e\u003cp\u003eFor the interpretation of HTC values, a ten-class classification scale, widely applied in the relevant literature, was used: HTC\u0026thinsp;\u0026lt;\u0026thinsp;0.4 \u0026ndash; extremely dry; 0.4\u0026thinsp;\u0026le;\u0026thinsp;HTC\u0026thinsp;\u0026lt;\u0026thinsp;0.8 \u0026ndash; very dry; 0.8\u0026thinsp;\u0026le;\u0026thinsp;HTC\u0026thinsp;\u0026lt;\u0026thinsp;1.1 \u0026ndash; dry; 1.1\u0026thinsp;\u0026le;\u0026thinsp;HTC\u0026thinsp;\u0026lt;\u0026thinsp;1.4 \u0026ndash; quite dry; 1.4\u0026thinsp;\u0026le;\u0026thinsp;HTC\u0026thinsp;\u0026lt;\u0026thinsp;1.7 \u0026ndash; optimal; 1.7\u0026thinsp;\u0026le;\u0026thinsp;HTC\u0026thinsp;\u0026lt;\u0026thinsp;2.1 \u0026ndash; quite humid; 2.1\u0026thinsp;\u0026le;\u0026thinsp;HTC\u0026thinsp;\u0026lt;\u0026thinsp;2.6 \u0026ndash; humid; 2.6\u0026thinsp;\u0026le;\u0026thinsp;HTC\u0026thinsp;\u0026lt;\u0026thinsp;3.0 \u0026ndash; very humid; HTC\u0026thinsp;\u0026ge;\u0026thinsp;3.0 \u0026ndash; extremely humid.\u003c/p\u003e\u003cp\u003eAll statistical computations and the preparation of maps and graphs were carried out using the R programming language.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eBetween 1950 and 2022, the mean length of the thermal growing season (TGS) across the study area was 189 days, exhibiting substantial spatial variability, ranging from 76 days in northern Scandinavia to 293 days in the southwestern part of the study area (the Netherlands) (Fig.\u0026nbsp;2a). In the 21st century, 69% of the seasons were longer than the average for the period 1950\u0026ndash;2000. Of the ten longest seasons, four occurred within the last five years (2019\u0026ndash;2022), while the remaining seasons took place in 1961, 1990, 2000, 2008, 2010, and 2011 thus, seven occurred in the 21st century. The TGS showed a tendency to lengthen towards the south and west.\u003c/p\u003e\u003cp\u003eThe mean onset date of the growing season during the study period was 24 April (Fig.\u0026nbsp;2b). The earliest onsets were observed in the southwestern part of the study area (the Netherlands, western Germany, northern France), occurring between 19 and 28 February. The latest onsets occurred in northern Scandinavia and the Scandinavian Mountains, between 19 and 28 June. Over the past two decades, only one season (2003) began later than the multi-year mean.\u003c/p\u003e\u003cp\u003eThe average termination date of the TGS was 30 October (Fig.\u0026nbsp;2c). The earliest terminations were recorded in northern Scandinavia and central Norway (17\u0026ndash;26 September), whereas the latest occurred in the Netherlands, Belgium, Denmark, and western Germany (6\u0026ndash;15 December). In the 21st century, 65% of the seasons ended later than the mean for the period 1950\u0026ndash;2000.\u003c/p\u003e\u003cp\u003e\u003cb\u003eFigure\u0026nbsp;2\u003c/b\u003e Mean length of thermal growing season (\u003cb\u003ea\u003c/b\u003e), mean date of start (\u003cb\u003eb\u003c/b\u003e), mean date of end (\u003cb\u003ec\u003c/b\u003e)\u003c/p\u003e\u003cp\u003eThe spatial pattern of mean air temperature during the TGS exhibits substantial variability across the study area (Fig.\u0026nbsp;3a). The mean air temperature for this period was 12.1\u0026deg;C. The lowest values were recorded in the northern part of Scandinavia, where the mean temperature ranged between 3\u0026ndash;5\u0026deg;C, whereas the highest values occurred in eastern Ukraine, in the southeastern portion of the study area, reaching 17\u0026ndash;19\u0026deg;C. Among the analyzed years, 1962 was the coldest (Fig.\u0026nbsp;3b), with a mean TGS air temperature of 10.8\u0026deg;C. The lowest values again occurred in northern Scandinavia (1\u0026ndash;3\u0026deg;C), while the highest were observed in southeastern Ukraine (16\u0026ndash;17\u0026deg;C). In contrast, 2018 was the warmest year of the study period (Fig.\u0026nbsp;3c), with a mean TGS air temperature of 13.7\u0026deg;C. The coolest areas during that year were located in the mountainous zone of southern Norway (7\u0026ndash;8\u0026deg;C), whereas the warmest areas encompassed the southern portion of the study area, including eastern Ukraine and northern Hungary, where mean temperatures reached 18\u0026ndash;20\u0026deg;C.\u003c/p\u003e\u003cp\u003eThe trend in air temperature during the thermal growing season was unequivocally positive across the entire study area (Fig.\u0026nbsp;3d). The average rate of temperature increase was 0.13\u0026deg;C/10 years. The lowest trend value (0.0\u0026deg;C/10 years) was observed in central Sweden, while the highest values (\u0026gt;\u0026thinsp;0.3\u0026deg;C/10 years) occurred in the southern part of the study area, particularly in Slovakia, Austria, and southern Germany. Notably, for more than 80% of the study area, the detected changes were statistically significant.\u003c/p\u003e\u003cp\u003e\u003cb\u003eFigure\u0026nbsp;3\u003c/b\u003e Mean air temperature (\u003cb\u003ea\u003c/b\u003e), coldest year in the study period, 1962 (\u003cb\u003eb\u003c/b\u003e), warmest year in the study period, 2018 (\u003cb\u003ec\u003c/b\u003e), rate of change (red dots \u0026ndash; statistically significant changes) (\u003cb\u003ed\u003c/b\u003e)\u003c/p\u003e\u003cp\u003eThe analysis of reference stations revealed substantial variability in mean air temperature during the TGS (Fig.\u0026nbsp;4). Mean air temperatures across the study period ranged from 11.2\u0026deg;C in Rovaniemi to 15.0\u0026deg;C in Kyiv. The remaining stations recorded the following values: 12.0\u0026deg;C in Copenhagen and Tallinn, 12.9\u0026deg;C in Munich, and 13.6\u0026deg;C in Warsaw. The lowest mean air temperature for a single growing season was recorded in 1987 in Rovaniemi, at 9.2\u0026deg;C, whereas the highest was observed in 1979 in Kyiv, with a mean seasonal temperature of 16.8\u0026deg;C. In the 21st century, the vast majority of growing seasons exhibited values exceeding the long-term mean. The proportion of seasons with above-average air temperature ranged from 55% in Copenhagen to 82% in Munich and as high as 86% in Tallinn. The rate of change in mean air temperature during the growing season varied among stations. The lowest rates were observed in Copenhagen and Tallinn (0.09\u0026deg;C/10 years), while the highest occurred in Munich (0.21\u0026deg;C/10 years). The remaining stations recorded rates of 0.10\u0026deg;C/10 years in Warsaw, 0.12\u0026deg;C/10 years in Kyiv, and 0.15\u0026deg;C/10 years in Rovaniemi. The statistical significance of these changes was confirmed for all stations except Copenhagen.\u003c/p\u003e\u003cp\u003e\u003cb\u003eFigure\u0026nbsp;4\u003c/b\u003e Variability and rate of change in air temperature during the thermal growing season\u003c/p\u003e\u003cp\u003eThe mean cumulative air temperature during the TGS was 2341\u0026deg;C (Fig.\u0026nbsp;5a). This value exhibited substantial spatial variability, ranging from 454\u0026deg;C in southern Norway to 4059\u0026deg;C in the southernmost part of the study area, encompassing northern Hungary. A systematic increase in cumulative air temperature was observed with progression towards the south. The lowest mean seasonal value of this indicator occurred in 1976 (Fig.\u0026nbsp;5b), amounting to 2052\u0026deg;C. The minimum value was recorded in northern Scandinavia (359\u0026deg;C), while the maximum was observed in northern Hungary (3891\u0026deg;C). In contrast, the highest mean seasonal cumulative air temperature was recorded in 2018 (Fig.\u0026nbsp;5c), when the mean for the entire study area reached 2786\u0026deg;C an increase of more than 400\u0026deg;C relative to the long-term average. That year, the lowest values were again found in northern Scandinavia (623\u0026deg;C), and the highest once more in northern Hungary (4626\u0026deg;C). All analyzed grid points exhibited a positive trend in cumulative air temperature over the study period (Fig.\u0026nbsp;5d). The average rate of increase was 53\u0026deg;C/10 years, with values ranging from 9\u0026deg;C to 131\u0026deg;C/10 years. Statistically significant changes were identified for 99% of the study area.\u003c/p\u003e\u003cp\u003e\u003cb\u003eFigure\u0026nbsp;5\u003c/b\u003e Cumulative air temperature (\u003cb\u003ea\u003c/b\u003e), lowest total in the study period, 1976 (\u003cb\u003eb\u003c/b\u003e), highest total in the study period, 2018 (\u003cb\u003ec\u003c/b\u003e), rate of change (red dots \u0026ndash; statistically significant changes) (\u003cb\u003ed\u003c/b\u003e)\u003c/p\u003e\u003cp\u003eThe analysis of cumulative air temperature during the TGS revealed a clear upward trend at all reference stations. Mean values of this indicator for the period 1950\u0026ndash;2022 ranged from 1671\u0026deg;C in Rovaniemi to 3339\u0026deg;C in Kyiv (Fig.\u0026nbsp;6). The remaining locations recorded the following values: 2332\u0026deg;C in Tallinn, 3072\u0026deg;C in Munich, 3099\u0026deg;C in Warsaw, and 3101\u0026deg;C in Copenhagen. The lowest seasonal value was observed in Rovaniemi in 1977 (1273\u0026deg;C), while the highest was recorded in Kyiv in 2010 (4000\u0026deg;C). In the 21st century, the proportion of seasons warmer than the long-term mean ranged from 86% in Tallinn to 95% in both Kyiv and Munich. All locations exhibited a statistically significant increase in cumulative air temperature during the TGS. The rate of change ranged from 51.1\u0026deg;C/10 years in Rovaniemi to 83.6\u0026deg;C/10 years in Munich. Correlation coefficients for these trends were statistically significant for all measurement sites.\u003c/p\u003e\u003cp\u003e\u003cb\u003eFigure\u0026nbsp;6\u003c/b\u003e Cumulative air temperature and its trends during the thermal growing season\u003c/p\u003e\u003cp\u003eThe mean total precipitation during the TGS in the period 1950\u0026ndash;2022 was 390 mm (Fig.\u0026nbsp;7a). The highest precipitation totals were recorded along the western coast of Norway, where they locally exceeded 1600 mm. In contrast, the lowest seasonal totals occurred in northern Scandinavia, where precipitation sums were below 200 mm. The spatial distribution indicates that higher precipitation totals are concentrated in mountainous areas and in proximity to large water bodies. The driest season of the entire study period was 1959 (Fig.\u0026nbsp;7b), with a mean precipitation total of 297 mm. The highest totals that year were again observed along the western coast of Norway (approximately 1200\u0026ndash;1400 mm), while the lowest occurred in central and northern Sweden, where local values did not exceed 100 mm. Conversely, the wettest season was recorded in 1998 (Fig.\u0026nbsp;7c), with a mean precipitation total of 487 mm for the entire study area. In that year, the minimum values were recorded in the far north of Norway (slightly above 100 mm), whereas the maximum occurred along the western coast of Norway and in the Alpine region, where local totals exceeded 1000 mm and in some places surpassed 1400 mm. Trend analysis revealed substantial spatial variability in the rate of change in precipitation totals (Fig.\u0026nbsp;7d). The mean trend for the entire area was \u0026minus;\u0026thinsp;1.1 mm/10 years, with the strongest decreases observed in southern Norway, locally exceeding 50 mm/10 years. The largest increases occurred in central Poland, Ukraine, and eastern Russia, where they surpassed 20 mm/10 years. Only about 10% of the study area exhibited statistically significant changes, indicating a relatively low consistency in the long-term direction of precipitation trends.\u003c/p\u003e\u003cp\u003e\u003cb\u003eFigure\u0026nbsp;7\u003c/b\u003e Total precipitation (\u003cb\u003ea\u003c/b\u003e), lowest total in the study period, 1959 (\u003cb\u003eb\u003c/b\u003e), highest total in the study period, 1998 (\u003cb\u003ec\u003c/b\u003e), rate of change (red dots \u0026ndash; statistically significant changes) (\u003cb\u003ed\u003c/b\u003e)\u003c/p\u003e\u003cp\u003eThe mean total precipitation during the TGS in the study period ranged from 285 mm in Rovaniemi to 736 mm in Munich (Fig.\u0026nbsp;8). The corresponding values for the remaining locations were: 365 mm in Kyiv, 385 mm in Tallinn, 386 mm in Warsaw, and 421 mm in Copenhagen. The highest seasonal precipitation total was recorded in Munich in 2002, amounting to 1042 mm, while the lowest was observed in Rovaniemi in 1969, with only 131 mm. The analysis indicated an increase in precipitation totals at all reference stations. However, the rate of change varied considerably by location, from 2.3 mm/10 years in Kyiv to 14.6 mm/10 years in Copenhagen. The high uncertainty associated with the trend estimates reflects substantial interannual variability in precipitation, which hinders an unambiguous interpretation of long-term changes. Among the stations analyzed, only Copenhagen and Munich exhibited statistically significant trends, suggesting that the observed increases in precipitation at these sites are the most robust and temporally consistent.\u003c/p\u003e\u003cp\u003e\u003cb\u003eFigure\u0026nbsp;8\u003c/b\u003e Total precipitation and its trends during the thermal growing season\u003c/p\u003e\u003cp\u003eThe number of days with precipitation during the thermal growing season (TGS) showed considerable spatial variation, ranging from 37 days in northern Scandinavia to 142 days along the western coast of Norway and in the Netherlands (Fig.\u0026nbsp;9a). The mean number of precipitation days across the study area was 73 days. Higher precipitation frequency was observed in mountainous regions and coastal zones, whereas the lowest values occurred in the continental interior. The lowest mean number of precipitation days in the TGS was recorded in 1955 (Fig.\u0026nbsp;9b), when the average for the entire study area was 58 days. Particularly low values occurred in central and northern Scandinavia, where the number of precipitation days locally dropped below 23. In contrast, the highest values that year were recorded in central Germany, exceeding 130 days. The year 1974 was characterised by the highest mean number of precipitation days during the growing season, reaching 89 days (Fig.\u0026nbsp;9c). The fewest precipitation days in 1974 occurred in northwestern Russia (slightly above 40 days), whereas the highest numbers were recorded in western Germany and the Netherlands, locally exceeding 190 days. The analysis of trends in the number of precipitation days revealed pronounced spatial variability (Fig.\u0026nbsp;9d). An increase in precipitation days dominated in the central and eastern parts of the study area, while a decrease occurred mainly in its northern and western parts. The average trend value was \u0026minus;\u0026thinsp;0.14 days/10 years. The most pronounced decline was observed in Denmark and southern Sweden, reaching \u0026minus;\u0026thinsp;4.1 days/10 years, whereas the largest increase occurred in central Poland, where the number of precipitation days rose by 4.2 days/10 years. Statistical significance of the detected changes was confirmed for only about 10% of the study area, primarily in regions where the changes were the greatest.\u003c/p\u003e\u003cp\u003e\u003cb\u003eFigure\u0026nbsp;9\u003c/b\u003e Number of days with precipitation (\u003cb\u003ea\u003c/b\u003e), lowest number of precipitation days during the study period in 1955 (\u003cb\u003eb\u003c/b\u003e), highest number of precipitation days during the study period in 1974 (\u003cb\u003ec\u003c/b\u003e), and trend in the number of precipitation days (red dots indicate statistically significant changes) (\u003cb\u003ed\u003c/b\u003e)\u003c/p\u003e\u003cp\u003eThe number of days with precipitation during the growing season (TGS) at the reference sites exhibited pronounced spatial variability (Fig.\u0026nbsp;10). In an average season, these values ranged from 56 days in Rovaniemi (northern Finland) to 112 days in Munich (southern Germany). At other locations, the number of precipitation days was as follows: 65 days in Kyiv, 74 days in Warsaw, 76 days in Tallinn, and 98 days in Copenhagen. It is noteworthy that despite Warsaw having a slightly higher mean precipitation sum, the number of precipitation days was lower than in Tallinn, where the total precipitation was marginally less. The lowest recorded number of precipitation days was observed in Rovaniemi in 1968 (35 days), whereas the highest was documented in Copenhagen in 2020 (164 days). Trend analysis of precipitation days revealed a positive rate of change across all analyzed stations except Tallinn, where no significant changes were detected (0.0 days/10 years). The highest rate of increase was identified in Copenhagen, amounting to 2.1 days/10 years. Despite the predominantly positive direction, trend estimates were characterized by considerable uncertainty. In many instances, the standard errors exceeded the trend estimates themselves, indicating that actual changes could potentially have been in the opposite, negative direction. The absence of statistically significant correlation coefficients for the trend across all investigated stations further corroborates the uncertainty regarding a definitive direction of change in the number of precipitation days over the studied period.\u003c/p\u003e\u003cp\u003e\u003cb\u003eFigure\u0026nbsp;10\u003c/b\u003e Temporal variation in the number of precipitation days and the rate of change during the thermal growing season\u003c/p\u003e\u003cp\u003eThe values of the HTC index over the analyzed multi-year period exhibited a pronounced dependence on topography and proximity to large water bodies. Higher values were recorded in areas situated at greater altitudes above sea level and near seas, whereas lower values occurred in lowland regions distant from water bodies, where a continental climate predominated. The mean HTC value for the studied area was 1.39, corresponding to optimal conditions for plant development (Fig.\u0026nbsp;11a). The lowest values were observed in central Ukraine and central Poland, where HTC ranged between 0.5 and 0.7, indicating very dry conditions. Conversely, the highest values were recorded in the Alps, the Carpathians, and particularly along the southwestern coast of Norway, where average values exceeded 3.0 and local maxima reached up to 5.2, indicative of extremely humid conditions. The driest season within the analyzed period was 1959 (Fig.\u0026nbsp;11b). The average HTC for the entire area that year was 0.98, indicating dry conditions. Index values ranged from 0.25 in central Sweden to 5.8 in northern Norway. In 1959, as much as 64% of the study area experienced conditions classified as extremely dry, very dry, or dry. In contrast, the highest seasonal HTC value was recorded in 1998 (Fig.\u0026nbsp;11c), when the mean value reached 1.85, corresponding to relatively humid conditions. The range of values was substantial, from 0 (extremely dry) in a small area of south-central Norway to 6.4 (extremely humid) in southern Norway. Analysis of temporal changes in HTC values revealed spatial heterogeneity (Fig.\u0026nbsp;11d). The southern and western parts of the study area exhibited a decreasing trend (negative), while northern and eastern regions showed an increasing trend (positive). The average trend value for the entire area was 0.00/10 years, indicating no significant overall change. Trend values varied from \u0026minus;\u0026thinsp;0.12/10 years in western Poland to 0.20/10 years in southern Norway. The statistical significance of the trend coefficient was confirmed over 14% of the study area, indicating limited, though locally pronounced changes.\u003c/p\u003e\u003cp\u003e\u003cb\u003eFigure\u0026nbsp;11\u003c/b\u003e Average HTC index value (\u003cb\u003ea\u003c/b\u003e), lowest HTC value during the multi-year period, year 1959 (\u003cb\u003eb\u003c/b\u003e), highest HTC index value during the multi-year period, year 1998 (\u003cb\u003ec\u003c/b\u003e), and rate of change (red dots indicate statistically significant changes) (\u003cb\u003ed\u003c/b\u003e)\u003c/p\u003e\u003cp\u003eAnalysis of the HTC index at selected reference points revealed significant spatial variability of this indicator over the analyzed multi-year period (Fig.\u0026nbsp;12). Mean values ranged from 0.96 in Kyiv, indicating dry conditions, to 2.01 in Munich, corresponding to humid conditions. At the remaining stations, HTC values were as follows: 1.12 in Copenhagen and Warsaw (quite dry conditions), 1.39 in Tallinn (optimal conditions), and 1.43 in Rovaniemi (also optimal conditions). The greatest inter-seasonal variability of the HTC index was observed in Rovaniemi, which exhibited both the lowest and highest values among all reference stations. In 1969, HTC reached 0.32, classified as extremely dry conditions, whereas in 1998 it peaked at 3.10, classified as extremely humid conditions. Changes in HTC values at the analyzed points were generally stable. Rates of change ranged from \u0026minus;\u0026thinsp;0.005/10 years in Munich to 0.03/10 years in Rovaniemi. In all cases, the estimates were characterized by considerable statistical uncertainty, suggesting that the direction of change may not be unequivocal. No statistically significant trends were detected at any of the analyzed stations.\u003c/p\u003e\u003cp\u003e\u003cb\u003eFigure\u0026nbsp;12\u003c/b\u003e Temporal variation of HTC index values and the rate of change during the thermal growing season\u003c/p\u003e"},{"header":"Summary and discussion","content":"\u003cp\u003eThermal and precipitation conditions in Central and Northern Europe during the thermal growing season (TGS) were analyzed for the multi-decadal period 1950\u0026ndash;2022. The analysis revealed significant spatial variability in TGS length, with a mean duration of 189 days and a pronounced upward trend. The length of the TGS ranged from 76 days in northern Scandinavia to 293 days in southwestern Europe. These results are consistent with previous studies on the growing season in Europe (Linderholm et al. \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Ruosteenoja et al. \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Aalto et al. \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). On average, the season commenced on 24 April and ended on 30 October, with both the onset and termination dates exhibiting temporal shifts towards earlier and later dates, respectively. Similar trends were reported by Szyga-Pluta et al. (\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), who demonstrated that in Poland, the growing season is expected to begin substantially earlier and end later. The authors estimated that, between 1966 and 2020, the onset of the growing season in Poland advanced by an average of approximately 3 days, while its termination was delayed by around 2 days.\u003c/p\u003e\u003cp\u003eDuring the study period, the mean air temperature throughout the thermal growing season (TGS) was 12.1\u0026deg;C, exhibiting pronounced spatial variability, ranging from 3\u0026ndash;5\u0026deg;C in northern Scandinavia to 17\u0026ndash;19\u0026deg;C in eastern Ukraine. The coldest year was 1962 (10.8\u0026deg;C), while the highest mean seasonal temperature was recorded in 2018 (13.7\u0026deg;C). Trend analysis revealed a clear and statistically significant increase in air temperature across most of the study area, with a mean rate of change of 0.13\u0026deg;C/10 years. The strongest warming was observed in the southern part of Central Europe. Among all reference stations analyzed, the highest positive trend was recorded in Munich, located in the southern portion of the study domain, where the rate of temperature increase reached 0.21\u0026deg;C/10 years. Comparable findings have been reported in numerous studies addressing the thermal conditions of the growing season (Bąk and Łabędzki \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Waldau et al. 2018; Xue et al. \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Similar results were presented by Tomczyk and Szyga-Pluta (\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), who, in their analysis of thermal and precipitation variability during the growing season in Poland, found that the mean air temperature in Warsaw ranged between 13.6 and 14.0\u0026deg;C. The present study corroborates this value, indicating that in Warsaw, the mean growing season temperature was 13.6\u0026deg;C. The rate of temperature increase reported by Tomczyk and Szyga-Pluta (\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) ranged from 0.15 to 0.19\u0026deg;C/10 years, whereas in the present analysis it amounted to 0.10\u0026deg;C/10 years.\u003c/p\u003e\u003cp\u003eCumulative air temperatures during the thermal growing season (TGS) exhibited a clear and statistically significant upward trend across Central\u0026ndash;Northern Europe in the period 1950\u0026ndash;2022. The mean cumulative temperature for the entire study area during the TGS was 2341\u0026deg;C, with pronounced spatial variability ranging from 454\u0026deg;C in southern Norway to over 4000\u0026deg;C in the southernmost parts of the region, including, among others, northern Hungary. These findings are consistent with earlier studies addressing changes in the thermal conditions of the growing season in Europe (Solantie \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2004\u003c/span\u003e; Verbai et al. \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Kanapickas et al. \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). In the study by Wypych et al. (\u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) on the spatial variability of cumulative growing season air temperatures in Poland, the mean value amounted to 3150\u0026deg;C, with the highest values recorded in the western part of the country. Comparable results were obtained in the present analysis, where cumulative temperatures for Poland ranged from 2800 to 3200\u0026deg;C, with maxima exceeding 3200\u0026deg;C in the western regions. Similar outcomes were also reported by Graczyk and Kundzewicz (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) and Tomczyk and Szyga-Pluta (\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe lowest mean cumulative air temperature during the thermal growing season (TGS) in the analyzed period occurred in 1976 (2052\u0026deg;C), whereas the highest was recorded in 2018 (2786\u0026deg;C). In the long-term perspective, the average rate of increase in this index amounted to 53\u0026deg;C/10 years, although considerable regional variability was observed, ranging from 9\u0026deg;C to 131\u0026deg;C/10 years. Statistically significant positive trends were recorded at all reference stations, with the highest rate observed in Munich (83.6\u0026deg;C/10 years). Comparable rates of change were reported by Spinoni et al. (\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2015\u003c/span\u003e), who, analyzing the period 1951\u0026ndash;2010, identified an increase in cumulative TGS air temperatures of 25\u0026deg;C/10 years in Scandinavia, 38\u0026deg;C/10 years in the Baltic States, and 48\u0026deg;C/10 years in Germany and Denmark. The results of the present study confirm these observations, with changes in Scandinavia ranging from 20\u0026ndash;40\u0026deg;C/10 years, in the Baltic States from 40\u0026ndash;60\u0026deg;C/10 years, and in Germany and Denmark exceeding 60\u0026deg;C/10 years.\u003c/p\u003e\u003cp\u003eThe observed increase clearly indicates a systematic warming of the thermal conditions of the growing season in Europe, with significant implications for both ecosystem functioning and agricultural production. Many plant species are highly dependent on cumulative air temperatures, which determine their phenological development as well as productivity. As air temperatures rise and the growing season lengthens in Central and Northern Europe, a northward expansion of thermophilous species such as maize (\u003cem\u003eZea mays\u003c/em\u003e), sunflower (\u003cem\u003eHelianthus annuus\u003c/em\u003e), grapevine (\u003cem\u003eVitis vinifera\u003c/em\u003e), and soybean (\u003cem\u003eGlycine max\u003c/em\u003e) can be anticipated, as confirmed by the findings of Desclaux and Roumet (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e1996\u003c/span\u003e), Dahmardeh (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2012\u003c/span\u003e), Khoufi et al. (\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2013\u003c/span\u003e), and Ortega-Farias and Riveros-Burgos (\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). At the same time, a gradual decline of species sensitive to heat stress, such as Norway spruce (\u003cem\u003ePicea abies\u003c/em\u003e) and European beech (\u003cem\u003eFagus sylvatica\u003c/em\u003e), is expected, as indicated by Sykes et al. (\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e1996\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eAnalysis of cumulative precipitation totals revealed pronounced spatial and interannual variability. The mean precipitation total for the entire study area was 390 mm, with the lowest values observed in northern Scandinavia (below 200 mm) and the highest along the western coast of Norway, where local totals exceeded 1600 mm. The driest growing season occurred in 1959, with a regional mean of 297 mm, while the wettest was recorded in 1998, with a total of 487 mm. The precipitation totals for Finland obtained in the present study, ranging from 200 to 400 mm during the thermal growing season (TGS), are consistent with the results reported by Ylh\u0026auml;isi et al. (\u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). These authors, analyzing data from regional climate simulations for the period 1961\u0026ndash;1990, demonstrated that the mean seasonal precipitation total in Finland (for April\u0026ndash;September) ranged from approximately 220 mm in the north to 420 mm in the southwest, values that correspond to the duration and characteristics of the TGS. These findings fall within the range obtained in the present analysis and confirm the presence of relatively low, yet regionally variable, precipitation totals during the TGS. On a regional scale, the results can also be compared with those of Tomczyk and Szyga-Pluta (\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), who reported that the average growing season precipitation total in Poland was 430 mm, with the lowest totals in central Poland and the highest in the south. These findings are consistent with the present study, in which the long-term mean precipitation total ranged from 200\u0026ndash;400 mm in central Poland to over 600 mm in the south.\u003c/p\u003e\u003cp\u003eThe conducted analysis revealed a mean decrease in cumulative precipitation of \u0026minus;\u0026thinsp;1.1 mm/10 years across the entire region, with local values reaching as low as \u0026minus;\u0026thinsp;50 mm/10 years in southern Norway. In contrast, the largest positive trends were observed in central Poland, Ukraine, and eastern Russia, locally reaching 20 mm/10 years. The statistical significance of these changes was low, with only approximately 10% of the analyzed area exhibiting significant trends, highlighting the high degree of interannual precipitation variability. Positive precipitation trends were recorded at all reference stations, although the rates varied from 2.3 mm/10 years in Kyiv to 14.6 mm/10 years in Copenhagen. Statistically significant trends were found only in Copenhagen and Munich, suggesting greater stability and reliability of precipitation changes in these locations. Similar spatial variability in precipitation trends across Europe has also been reported by Van den Besselaar et al. (\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2013\u003c/span\u003e), Caloiero et al. (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), and Zeder and Fischer (\u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe number of days with precipitation during the TGS period exhibited pronounced spatial and temporal variability. The mean number of days with precipitation across the entire study area was 73 days, ranging from 37 days in northern Scandinavia to 142 days along the western coast of Norway and in the Netherlands. Numerous regional studies conducted in Central and Northern Europe corroborate these findings (Bengtsson and Rana 2013; Br\u0026aacute;zdil et al. \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Repel et al. \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). In the wettest season (1974), the local number of days with precipitation exceeded 190 days, whereas in the driest season (1955), it fell below 23 days in northern Scandinavia. Over the analyzed multi-year period, the mean trend amounted to -0.14 days/10 years, with regional variations ranging from decreases of 4.1 days/10 years in Denmark and southern Sweden to increases of 4.2 days/10 years in central Poland. These findings are supported by Skowera et al. (\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2014\u003c/span\u003e), who, in their analysis of trends in the number of days with precipitation from April to September, reported an increase of 4.14 days/10 years for the Poznań area in central-western Poland. At reference stations, the number of days with precipitation ranged from 56 days in Rovaniemi to 112 days in Munich, with the highest positive trend observed in Copenhagen (2.1 days/10 years). Despite the predominance of positive trends, their statistical significance was low, and the high uncertainty of the estimates indicates no clear directional change in the number of precipitation days over the study period.\u003c/p\u003e\u003cp\u003eThe analysis of the hydrothermal coefficient (HTC) during the TGS period revealed significant spatial variability, primarily driven by topography and proximity to water bodies. The mean HTC value across the entire study area was 1.39, corresponding to optimal conditions for plant growth. The lowest values (0.5\u0026ndash;0.7) were recorded in central Poland and Ukraine, indicating very dry conditions, whereas the highest values (above 3.0, with a maximum of 6.4) occurred in mountainous and coastal regions such as the Alps and southwestern Norway. Based on the data presented by Chmist-Sikorska and Struzik (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), meteorological droughts in Poland from April to September occur relatively frequently, with their frequency and intensity exhibiting regional variability, being most pronounced in the central part of the country. According to the analyzed HTC indicator, these droughts corresponded to values within the range of 0.4\u0026ndash;0.8 (very dry conditions) and below 0.4 (extremely dry conditions). The results of the present analysis are consistent with the study by Evarte-Bundere and Evarts-Bunders (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2012\u003c/span\u003e), who assessed the HTC in Latvia and reported mean values ranging from 1.3 to 2.0 during the growing season, indicating optimal conditions.\u003c/p\u003e\u003cp\u003eThe driest season occurred in 1959 (mean HTC: 0.98), whereas the wettest season was in 1998 (mean HTC: 1.85). Over the analyzed multi-year period, no significant changes were observed at the scale of the entire study area (0.00/10 years); however, local trends included both positive and negative variations, ranging from 0.12/10 years in western Poland to 0.20/10 years in southern Norway. Similar patterns were reported by Chmist-Sikorska et al. (2022) and Samborski (\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), who analyzed HTC for the Zamość region (southeastern Poland) and found a decreasing trend of 0.13/10 years. At reference stations, HTC values ranged from 0.96 in Kyiv (dry conditions) to 2.01 in Munich (humid conditions), with the highest seasonal variability observed in Rovaniemi, where HTC fluctuated between 0.32 and 3.10. Despite regional variability, none of the observed trends reached statistical significance, indicating the absence of clear and persistent changes in hydrothermal conditions over the study period.\u003c/p\u003e\u003cp\u003eThe HTC indicator accounts for both precipitation totals and air temperature patterns, providing a more comprehensive measure of hydrothermal conditions than indices based solely on one of these factors. Knowledge of HTC values and trends in a given region enables informed adaptation of crop and tree species selection to changing climatic conditions. In areas exhibiting an increasing HTC trend, the introduction of species more sensitive to water deficit, requiring higher moisture and temperature, may be feasible. Conversely, in regions with a decreasing HTC trend, it is advisable to limit the cultivation of water-demanding species in favor of more drought-tolerant and thermally resilient plants.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e FM was involved in conceptualisation, data curation, formal analysis, investigation, methodology, project administration, resources, software, validation, visualisation and writing.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e No funding was received for conducting this study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e The data presented in this study are openly available at https://surfobs.climate.copernicus.eu/dataaccess/access_eobs.php#datafiles\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of interest\u003c/strong\u003e The authors assert that they do not have any known conflicting financial interests or personal affiliations that might have appeared to impact the research presented in this paper.\u003c/p\u003e\n"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAalto J, Pirinen P, Kauppi PE, Rantanen M, Lussana C, Lyytik\u0026auml;inen-Saarenmaa P, Gregow H (2022) High-resolution analysis of observed thermal growing season variability over northern Europe. Clim Dyn 58:1477\u0026ndash;1493. https://doi.org/10.1007/s00382-021-05970-y\u003c/li\u003e\n\u003cli\u003eAnderson R, Bayer PE, Edwards D (2020) Climate change and the need for agricultural adaptation. Curr Opin Plant Biol 56:197\u0026ndash;202. https://doi.org/10.1016/j.pbi.2019.12.006\u003c/li\u003e\n\u003cli\u003eBąk B, Łabędzki L (2014) Thermal conditions in Bydgoszcz Region in growing seasons of 2011\u0026ndash;2050 in view of expected climate change. J Water Land Dev. https://doi.org/10.1515/jwld-2014-0026\u003c/li\u003e\n\u003cli\u003eBengtsson L, Rana A (2014) Long‐term change of daily and multi‐daily precipitation in southern Sweden. Hydrol Process 28:2897\u0026ndash;2911. https://doi.org/10.1002/hyp.9774\u003c/li\u003e\n\u003cli\u003eBr\u0026aacute;zdil R, Zahradn\u0026iacute;ček P, Dobrovoln\u0026yacute; P, \u0026Scaron;těp\u0026aacute;nek P, Trnka M (2021) Observed changes in precipitation during recent warming: The Czech Republic, 1961\u0026ndash;2019. Int J Climatol 41:3881\u0026ndash;3902. https://doi.org/10.1002/joc.7048\u003c/li\u003e\n\u003cli\u003eCaloiero T, Caloiero P, Frustaci F (2018) Long‐term precipitation trend analysis in Europe and in the Mediterranean basin. Water Environ J 32:433\u0026ndash;445. https://doi.org/10.1111/wej.12346\u003c/li\u003e\n\u003cli\u003eCarter TR (1998) Changes in the thermal growing season in Nordic countries during the past century and prospects for the future. Agric For Meteorol 7:161\u0026ndash;179. https://doi.org/10.23986/afsci.72857\u003c/li\u003e\n\u003cli\u003eChallinor AJ, Watson J, Lobell DB, Howden SM, Smith DR, Chhetri N (2014) A meta-analysis of crop yield under climate change and adaptation. Nat Clim Change 4:287\u0026ndash;291. https://doi.org/10.1038/nclimate2153\u003c/li\u003e\n\u003cli\u003eChange C (2016) Agriculture and food security. In: The State of Food and Agriculture. FAO, Rome\u003c/li\u003e\n\u003cli\u003eChmist-Sikorska J, Struzik P (2022) Agricultural drought assessment on the base of Hydro-thermal Coefficient of Selyaninov in Poland. Ital J Agrometeorol 1:3\u0026ndash;12. https://doi.org/10.36253/ijam-1530\u003c/li\u003e\n\u003cli\u003eDahmardeh M (2012) Effects of sowing date on the growth and yield of maize cultivars (Zea mays L.) and the growth temperature requirements. Afr J Biotechnol 11:12450\u0026ndash;12453. https://doi.org/10.5897/AJB12.201\u003c/li\u003e\n\u003cli\u003eDesclaux D, Roumet P (1996) Impact of drought stress on the phenology of two soybean (Glycine max L. Merr) cultivars. Field Crops Res 46:61\u0026ndash;70. https://doi.org/10.1016/0378-4290(95)00086-0\u003c/li\u003e\n\u003cli\u003eDong M, Jiang Y, Zheng C, Zhang D (2012) Trends in the thermal growing season throughout the Tibetan Plateau during 1960\u0026ndash;2009. Agric For Meteorol 166:201\u0026ndash;206. https://doi.org/10.1016/j.agrformet.2012.07.013\u003c/li\u003e\n\u003cli\u003eEck MA, Murray AR, Ward AR, Konrad CE (2020) Influence of growing season temperature and precipitation anomalies on crop yield in the southeastern United States. Agric For Meteorol 291:108053. https://doi.org/10.1016/j.agrformet.2020.108053\u003c/li\u003e\n\u003cli\u003eEvarte-Bundere G, Evarts-Bunders P (2012) Using of the hydrothermal coefficient (HTC) for interpretation of distribution of non-native tree species in Latvia on example of cultivated species of genus Tilia. Acta Biol Univ Daugavp 12:135\u0026ndash;148\u003c/li\u003e\n\u003cli\u003eFern\u0026aacute;ndez ME, Gyenge JE, Varela S, de Urquiza M (2014) Effects of the time of drought occurrence within the growing season on growth and survival of Pinus ponderosa seedlings. Trees 28:745\u0026ndash;756. https://doi.org/10.1007/s00468-014-0986-1\u003c/li\u003e\n\u003cli\u003eFrich PALV, Alexander LV, Della-Marta P, Gleason B, Haylock M, Tank AK, Peterson T (2002) Observed coherent changes in climatic extremes during the second half of the twentieth century. Clim Res 19(3): 193-212. https://doi.org/10.3354/cr019193\u003c/li\u003e\n\u003cli\u003eFu YH, Piao S, Op de Beeck M, Cong N, Zhao H, Zhang Y, Menzel A, Janssens IA (2014) Recent spring phenology shifts in western Central Europe based on multiscale observations. Glob Ecol Biogeogr 23:1255\u0026ndash;1263. https://doi.org/10.1111/geb.12210\u003c/li\u003e\n\u003cli\u003eGraczyk D, Kundzewicz ZW (2016) Changes of temperature-related agroclimatic indices in Poland. Theor Appl Climatol 124:401\u0026ndash;410. https://doi.org/10.1007/s00704-015-1429-7\u003c/li\u003e\n\u003cli\u003eGrigorieva E (2020) Evaluating the sensitivity of growing degree days as an agro-climatic indicator of the climate change impact: A case study of the Russian far East. Atmosphere 11:404. https://doi.org/10.3390/atmos11040404\u003c/li\u003e\n\u003cli\u003eGrusson Y, Wesstr\u0026ouml;m I, Joel A (2021) Impact of climate change on Swedish agriculture: Growing season rain deficit and irrigation need. Agric Water Manag 251:106858. https://doi.org/10.1016/j.agwat.2021.106858\u003c/li\u003e\n\u003cli\u003eHaylock MR, Hofstra N, Klein Tank AMG, Klok EJ, Jones PD, New M (2008) A European daily high-resolution gridded data set of surface temperature and precipitation for 1950\u0026ndash;2006. J Geophys Res 113:D20119. https://doi.org/10.1029/2008JD010201\u003c/li\u003e\n\u003cli\u003eH\u0026oslash;gda KA, T\u0026oslash;mmervik H, Karlsen SR (2013) Trends in the start of the growing season in Fennoscandia 1982\u0026ndash;2011. Remote Sens 5:4304\u0026ndash;4318. https://doi.org/10.3390/rs5094304\u003c/li\u003e\n\u003cli\u003eIler AM, CaraDonna PJ, Forrest JR, Post E (2021) Demographic consequences of phenological shifts in response to climate change. Annu Rev Ecol Evol Syst 52:221\u0026ndash;245. https://doi.org/10.1146/annurev-ecolsys-011921-032939\u003c/li\u003e\n\u003cli\u003eIPCC (2021) Climate change 2021: the physical science basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge University Press, Cambridge\u003c/li\u003e\n\u003cli\u003eJanni M, Maestri E, Gull\u0026igrave; M, Marmiroli M, Marmiroli N (2024) Plant responses to climate change, how global warming may impact on food security: a critical review. Front Plant Sci 14:1297569. https://doi.org/10.3389/fpls.2023.1297569\u003c/li\u003e\n\u003cli\u003eKalvāns A, Kalvāne G, Zandersons V, Gaile D, Briede A (2023) Recent seasonally contrasting and persistent warming trends in Latvia. Theor Appl Climatol 154:125\u0026ndash;139. https://doi.org/10.1007/s00704-023-04540-y\u003c/li\u003e\n\u003cli\u003eKanapickas A, Vagusevičienė I, Juknys R, Sujetovienė G (2022) Effects of climatic and cultivar changes on winter wheat phenology in central Lithuania. Int J Biometeorol 66:2009\u0026ndash;2020. https://doi.org/10.1007/s00484-022-02336-9\u003c/li\u003e\n\u003cli\u003eKarlsen SR, Stendardi L, T\u0026oslash;mmervik H, Nilsen L, Arntzen I, Cooper EJ (2021) Time-series of cloud-free Sentinel-2 NDVI data used in mapping the onset of growth of central Spitsbergen, Svalbard. Remote Sens 13:3031. https://doi.org/10.3390/rs13153031\u003c/li\u003e\n\u003cli\u003eKarlsen SR, Elvebakk A, T\u0026oslash;mmervik H, Belda S, Stendardi L (2022) Changes in onset of vegetation growth on Svalbard, 2000\u0026ndash;2020. Remote Sens 14:6346. https://doi.org/10.3390/rs14246346\u003c/li\u003e\n\u003cli\u003eKejna M, Rudzki M (2021) Spatial diversity of air temperature changes in Poland in 1961\u0026ndash;2018. Theor Appl Climatol 143:1361\u0026ndash;1379. https://doi.org/10.3390/atmos13081232\u003c/li\u003e\n\u003cli\u003eKetzler G, R\u0026ouml;mer W, Beylich AA (2020) The climate of Norway. In: Landscapes and Landforms of Norway. Springer, Cham, pp 7\u0026ndash;29. https://doi.org/10.1007/978-3-030-52563-7_2\u003c/li\u003e\n\u003cli\u003eKhoufi S, Khamassi K, Teixeira da Silva JA, Aoun N, Rezgui S, Ben Jeddi F (2013) Assessment of diversity of phenologically and morphologically related traits among adapted populations of sunflower (Helianthus annuus L.). Helia 36:29\u0026ndash;40. https://doi.org/10.2298/HEL1358029K\u003c/li\u003e\n\u003cli\u003eKollo J, Metslaid S, Padari A, Hordo M, Kangur A, Noe SM (2023) Trends in thermal growing season length from years 1955\u0026ndash;2020 \u0026ndash; A case study in hemiboreal forest in Estonia. Boreal Environ Res 28:169\u0026ndash;180\u003c/li\u003e\n\u003cli\u003eKożuchowski K (2011) Klimat Polski: nowe spojrzenie. Wydawnictwo Naukowe PWN, Warszawa (in Polish)\u003c/li\u003e\n\u003cli\u003eKramer PJ (1983) Problems in water relations of plants and cells. Int Rev Cytol 85:253\u0026ndash;286. https://doi.org/10.1016/S0074-7696(08)62375-X\u003c/li\u003e\n\u003cli\u003eLakson M, Post P, Sepp M (2019) The impact of atmospheric circulation on air temperature rise in Estonia. Front Earth Sci 7:131. https://doi.org/10.3389/feart.2019.00131\u003c/li\u003e\n\u003cli\u003eLi L, Hao Y, Zheng Z, Wang W, Biederman JA, Wang Y, Xu Z (2022) Heavy rainfall in peak growing season had larger effects on soil nitrogen flux and pool than in the late season in a semiarid grassland. Agric Ecosyst Environ 326:107785. https://doi.org/10.1016/j.agee.2021.107785\u003c/li\u003e\n\u003cli\u003eLinderholm HW, Walther A, Chen D (2008) Twentieth-century trends in the thermal growing season in the Greater Baltic Area. Clim Change 87:405\u0026ndash;419. https://doi.org/10.1007/s10584-007-9327-3\u003c/li\u003e\n\u003cli\u003eLohtander A, R\u0026auml;is\u0026auml;nen DJ (2024) Changes in diurnal temperature range in Finland between 1961\u0026ndash;1990 and 1991\u0026ndash;2020\u003c/li\u003e\n\u003cli\u003eLu M, Sun H, Yan D, Xue J, Yi S, Gui D, Zhang W (2021) Projections of thermal growing season indices over China under global warming of 1.5 \u0026deg;C and 2.0 \u0026deg;C. Sci Total Environ 781:146774. https://doi.org/10.1016/j.scitotenv.2021.146774\u003c/li\u003e\n\u003cli\u003eMiś F, Tomczyk AM (2025) Spatial and temporal differentiation of the thermal growing season in central and northern Europe. Theor Appl Climatol 156:1\u0026ndash;14. https://doi.org/10.1007/s00704-025-05382-6\u003c/li\u003e\n\u003cli\u003eNidzgorska-Lencewicz J, Mąkosza A, Koźmiński C, Michalska B (2024) Potential risk of frost in the growing season in Poland. Agriculture 14:501. https://doi.org/10.3390/agriculture14030501\u003c/li\u003e\n\u003cli\u003eNing X, Liu G, Zhang L, Qin X, Zhou S, Qin Y (2017) The spatio-temporal variations of frost-free period in China from 1951 to 2012. J Geogr Sci 27:23\u0026ndash;42. https://doi.org/10.1007/s11442-017-1362-z\u003c/li\u003e\n\u003cli\u003eOrtega-Farias S, Riveros-Burgos C (2019) Modeling phenology of four grapevine cultivars (Vitis vinifera L.) in Mediterranean climate conditions. Sci Hortic 250:38\u0026ndash;44. https://doi.org/10.1016/j.scienta.2019.02.025\u003c/li\u003e\n\u003cli\u003ePerer J (2023) Effect of global warming on agricultural productivity. Int J Agric\u003c/li\u003e\n\u003cli\u003ePiao S, Liu Q, Chen A, Janssens IA, Fu Y, Dai J, Zhu X (2019) Plant phenology and global climate change: Current progresses and challenges. Glob Change Biol 25:1922\u0026ndash;1940. https://doi.org/10.1111/gcb.14619\u003c/li\u003e\n\u003cli\u003eRepel A, Zeleň\u0026aacute;kov\u0026aacute; M, Jothiprakash V, Hlavat\u0026aacute; H, Bli\u0026scaron;ťan P, Gargar I, Purcz P (2021) Long-term analysis of precipitation in Slovakia. Water 13:952. https://doi.org/10.3390/w13070952\u003c/li\u003e\n\u003cli\u003eRu J, Zhou Y, Hui D, Zheng M, Wan S (2018) Shifts of growing‐season precipitation peaks decrease soil respiration in a semiarid grassland. Glob Change Biol 24:1001\u0026ndash;1011. https://doi.org/10.1111/gcb.13941\u003c/li\u003e\n\u003cli\u003eRuosteenoja K, R\u0026auml;is\u0026auml;nen J, Pirinen P (2011) Projected changes in thermal seasons and the growing season in Finland. Int J Climatol 31:1473\u0026ndash;1487. https://doi.org/10.1002/joc.2171\u003c/li\u003e\n\u003cli\u003eRybashlykova LP (2025) Variability in hydrothermal coefficient (HTC) and productivity of pasture ecosystems of Tersko-Kuma lowland, Russia. J Agrometeorol 27:236\u0026ndash;238. https://doi.org/10.54386/jam.v27i2.2653\u003c/li\u003e\n\u003cli\u003eSalmi T, M\u0026auml;\u0026auml;tt\u0026auml; A, Anttila P, Ruoho-Airola T, Amnell T (2002) Detecting trends of annual values of atmospheric pollutants by the Mann-Kendall test and Sen\u0026apos;s slope estimates \u0026ndash; the Excel template application MAKESENS. Publ Air Qual 31, Finnish Meteorological Institute, Helsinki\u003c/li\u003e\n\u003cli\u003eSamborski AS (2024) Agroclimatic characterization of Zamosc, Poland using hydrothermal coefficient (HTC). J Agrometeorol 26:473\u0026ndash;476. https://doi.org/10.54386/jam.v26i4.2655\u003c/li\u003e\n\u003cli\u003eSchmitt J, Offermann F, S\u0026ouml;der M, Fr\u0026uuml;hauf C, Finger R (2022) Extreme weather events cause significant crop yield losses at the farm level in German agriculture. Food Policy 112:102359. https://doi.org/10.1016/j.foodpol.2022.102359 \u003c/li\u003e\n\u003cli\u003eSelyaninov GT (1930) Method of determination of agricultural climate characteristic. Trans Agric Meteorol 21\u003c/li\u003e\n\u003cli\u003eSkowera B, Kopcińska J, Kopeć B (2014) Changes in thermal and precipitation conditions in Poland in 1971\u0026ndash;2010. Ann Warsaw Univ Life Sci-SGGW Land Reclam 46. https://doi.org/10.2478/sggw-2014-0013\u003c/li\u003e\n\u003cli\u003eSolantie R (2004) Daytime temperature sum \u0026ndash; a new thermal variable describing growing season characteristics and explaining evapotranspiration. Boreal Environ Res 9:319\u003c/li\u003e\n\u003cli\u003eSpinoni J, Naumann G, Vogt J, Barbosa P. (2015) European drought climatologies and trends based on a multi-indicator approach. Global and Planetary Change 127: 50-57. https://doi.org/10.1016/j.gloplacha.2015.01.012\u003c/li\u003e\n\u003cli\u003eStrong C, McCabe GJ (2017) Observed variations in U.S. frost timing linked to atmospheric circulation patterns. Nat Commun 8:15307. https://doi.org/10.1038/ncomms15307\u003c/li\u003e\n\u003cli\u003eSykes MT, Prentice IC, Cramer W (1996) A bioclimatic model for the potential distributions of north European tree species under present and future climates. J Biogeogr:203\u0026ndash;233\u003c/li\u003e\n\u003cli\u003eSzyga-Pluta K (2022) Assessment of changing agroclimatic conditions in Poland based on selected indicators. Atmosphere 13:1232\u003c/li\u003e\n\u003cli\u003eSzyga-Pluta K, Tomczyk AM, Piniewski M, Eini MR (2023) Past and future changes in the start, end, and duration of the growing season in Poland. Acta Geophys 71:3041\u0026ndash;3055. https://doi.org/10.1007/s11600-023-01117-1\u003c/li\u003e\n\u003cli\u003eTomczyk AM, Szyga-Pluta K (2019) Variability of thermal and precipitation conditions in the growing season in Poland in the years 1966\u0026ndash;2015. Theor Appl Climatol 135:1517\u0026ndash;1530. https://doi.org/10.1007/s00704-018-2450-4\u003c/li\u003e\n\u003cli\u003eVan den Besselaar EJM, Klein Tank AMG, Buishand TA (2013) Trends in European precipitation extremes over 1951\u0026ndash;2010. Int J Climatol 33. https://doi.org/10.1002/joc.3619\u003c/li\u003e\n\u003cli\u003eVerbai Z, Lazar I, Kalm\u0026aacute;r F (2014) Heating degree day in Hungary. Environ Eng Manag J 13:6\u003c/li\u003e\n\u003cli\u003eWaldau T, Chmielewski FM (2018) Spatial and temporal changes of spring temperature, thermal growing season and spring phenology in Germany 1951\u0026ndash;2015. https://doi.org/10.1127/metz/2018/0923\u003c/li\u003e\n\u003cli\u003eWalther A, Linderholm HW (2006) A comparison of growing season indices for the Greater Baltic Area. Int J Biometeorol 51:107\u0026ndash;118. https://doi.org/10.1007/s00484-006-0048-5\u003c/li\u003e\n\u003cli\u003eWypych A, Sulikowska A, Ustrnul Z, et al. (2017) Variability of growing degree days in Poland in response to ongoing climate changes in Europe. Int J Biometeorol 61:49\u0026ndash;59. https://doi.org/10.1007/s00484-016-1190-3\u003c/li\u003e\n\u003cli\u003eXue F, Jiang Y, Wang M, Dong M, Ding X, Yang X, Kang M (2020) Temperature and thermal growing season variations along elevational gradients on a sub-alpine, temperate China. Theor Appl Climatol 140:15\u0026ndash;24. https://doi.org/10.1007/s00704-019-03067-5\u003c/li\u003e\n\u003cli\u003eYin Y, Deng H, Wu S (2019) Spatial-temporal variations in the thermal growing degree-days and season under climate warming in China during 1960\u0026ndash;2011. Int J Biometeorol 63:649\u0026ndash;658. https://doi.org/10.1007/s00484-017-1417-y\u003c/li\u003e\n\u003cli\u003eYlh\u0026auml;isi JS, Tiet\u0026auml;v\u0026auml;inen H, Peltonen-Sainio P, Ven\u0026auml;l\u0026auml;inen A, Eklund J, R\u0026auml;is\u0026auml;nen J, Jylh\u0026auml; K (2010) Growing season precipitation in Finland under recent and projected climate. Nat Hazards Earth Syst Sci 10:1563\u0026ndash;1574. https://doi.org/10.5194/nhess-10-1563-2010\u003c/li\u003e\n\u003cli\u003eZeder J, Fischer EM (2020) Observed extreme precipitation trends and scaling in Central Europe. Weather Clim Extremes 29:100266. https://doi.org/10.1016/j.wace.2020.100266\u003c/li\u003e\n\u003cli\u003eZhang Q, Hu Z (2018) Assessment of drought during corn growing season in Northeast China. Theor Appl Climatol 133:1315\u0026ndash;1321. https://doi.org/10.1007/s00704-018-2469-6\u003c/li\u003e\n\u003cli\u003eZhu L, Yan X (2023) Change and attribution of frost days and frost‐free periods in China. Int J Climatol 43:7935\u0026ndash;7953. https://doi.org/10.1002/joc.8310\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":true,"isPdf":false,"isPdfUpToDate":false,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"acta-geophysica","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"agph","sideBox":"Learn more about [Acta Geophysica](http://link.springer.com/journal/11600)","snPcode":"11600","submissionUrl":"https://www.editorialmanager.com/agph/default2.aspx","title":"Acta Geophysica","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Growing season, Temperature, Europe, Precipitation, Climate change","lastPublishedDoi":"10.21203/rs.3.rs-7487162/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7487162/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe study analyzed thermal and precipitation conditions during the thermal growing season (TGS) in Central and Northern Europe over the period 1950\u0026ndash;2022. The mean season length was 189 days, with substantial spatial variability ranging from 76 days in northern Scandinavia to 293 days in the southwestern part of the study area. A statistically significant increase in season length was observed over the study period. On average, the season commenced on April 24 and ended on October 30, with its onset and termination shifting towards earlier and later dates, respectively. The mean air temperature during the TGS was 12.1\u0026deg;C, increasing at a rate of 0.13\u0026deg;C/10 years, while the sum of annual temperatures rose on average by 53\u0026deg;C/10 years. The highest rates of change were recorded in the southern part of Central Europe. Precipitation totals during the growing season exhibited pronounced spatial and seasonal variability, with a mean value of 390 mm and a weak decreasing trend (\u0026ndash;1.1 mm/10 years). The number of days with precipitation averaged 73, while values of the Hydrothermal Coefficient of Selyaninov (HTC) ranged from 0.5 to over 3.0, with a mean of 1.39, corresponding to optimal conditions for plant development. HTC trends were regionally differentiated but statistically insignificant for the study area as a whole. The results indicate a systematic warming of the growing season and its implications for ecosystem functioning and agricultural production in Europe.\u003c/p\u003e","manuscriptTitle":"Thermal and Precipitation Conditions during the Thermal Growing Season in Central and Northern Europe","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-09-11 00:07:57","doi":"10.21203/rs.3.rs-7487162/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewerAgreed","content":"","date":"2025-09-04T12:54:31+00:00","index":0,"fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-09-04T10:18:43+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"Acta Geophysica","date":"2025-09-02T10:06:46+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-09-02T06:22:30+00:00","index":"","fulltext":""},{"type":"submitted","content":"Acta Geophysica","date":"2025-08-31T11:21:50+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"acta-geophysica","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"agph","sideBox":"Learn more about [Acta Geophysica](http://link.springer.com/journal/11600)","snPcode":"11600","submissionUrl":"https://www.editorialmanager.com/agph/default2.aspx","title":"Acta Geophysica","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"fd41db9e-48ce-4d9f-a62b-90a2f04d8911","owner":[],"postedDate":"September 11th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-12-08T16:06:36+00:00","versionOfRecord":{"articleIdentity":"rs-7487162","link":"https://doi.org/10.1007/s11600-025-01730-2","journal":{"identity":"acta-geophysica","isVorOnly":false,"title":"Acta Geophysica"},"publishedOn":"2025-12-05 15:57:58","publishedOnDateReadable":"December 5th, 2025"},"versionCreatedAt":"2025-09-11 00:07:57","video":"","vorDoi":"10.1007/s11600-025-01730-2","vorDoiUrl":"https://doi.org/10.1007/s11600-025-01730-2","workflowStages":[]},"version":"v1","identity":"rs-7487162","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7487162","identity":"rs-7487162","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}