Spatiotemporal trend analysis of climate indices for the European continent

The occurrence of unanticipated catastrophic extreme weather events as a result of an uncertain climate causes human fatalities, devastation, ecosystem impacts, and economic losses in all areas of the world (Eyshi Rezaei et al. 2015; Sergio et al. 2018). Understanding such extreme events has become increasingly important in the last two decades because of their consequences (Sillmann et al. 2013). Climate data are needed for monitoring and management and can be summarized using a set of standardized useful climate metrics. Trends in past and future climate data have been extensively researched to better understand how a climate influences the frequency and intensity of extreme weather occurrences (Razavi et al. 2016). Analysis of future climate trends is necessary for future climate risk assessments, and the analysis of historical observational data is critical for understanding the current consequences of global warming in comparison to the past (Rimbu et al. 2014, 2017; Felix et al. 2021). Long-term monthly or seasonal averages, such as gridded global temperature or precipitation datasets are often used in climate change research (Brohan et al. 2006; Smith & Reynolds 2005). The assessment of not only the features of the mean climate but also extreme climate and weather occurrences is an essential aspect in modern climatology. Climate change, variability, and extreme events are important aspects of society (Ruiz-Alvarez et al. 2020).

The fifth assessment report (AR5) of the Intergovernmental Panel on Climate Change (IPCC) particularly in the chapter titled ‘Observations: Atmosphere and Surface’ evaluated the research on regional and global changes in climate extremes (Hartmann et al. 2013). The summary stated that there is limited evidence of changes in extremes associated with other climate variables since the mid-20th century (Hartmann et al. 2013). One of the most serious challenges to humanity is the rise in global temperature and its consequences (Outten & Sobolowski 2021). From 1880 to 2012, global land and ocean surface temperature increased by 0.65–1.06 °C, resulting in the warming of Earth and exhibits substantial inter-annual to decadal fluctuations (Hartmann et al. 2013). Observed global climate changes are unprecedented, such as the warming of atmosphere, decreased ice and snow, and increasing sea level (IPCC 2014). Understanding temperature extremes is crucial for assessing the impacts of climate change (Almazroui et al. 2014). The most important effects of global warming would be an increase in the size of occurrence of severe precipitation events caused by higher atmospheric moisture levels, thunderstorms, or large-scale storm activity (Roy & Balling 2004). Average global precipitation is expected to grow by 2–3% for every 1 °C in temperature (Schneider et al. 2010). The fraction of severe precipitation in relation to total yearly precipitation is increasing (Alexander et al. 2006). Droughts are becoming more common worldwide because precipitation in areas is too low in the expected mean for a given period (Dai 2011; Trenberth et al. 2014). One of the findings indicates a high level of uncertainty in global scale drought patterns during the last 60 years, with no indication of a rise in the overall area impacted by droughts (Seneviratne 2012). The IPCC assessment identified significant uncertainty in drought data, and conflicting results from research on several regional drought patterns (Seneviratne et al. 2012).

Agro-climatic zones – weather extremes that occurred in historical scenarios of the selected weather data; significant seasonal variations in global radiation were also potential crucial criteria for future climate forecasts (Kahiluoto et al. 2014; Mäkinen et al. 2018). Heat stress, with a combined effect of temperature and relative humidity, is likely to rise as the global climate warms. Studies on the relative humidity relevance to the death rates are less common, but resulting in poor health has already been discovered (Callan et al. 1993). A link of fatalities between total relative humidity and excess winter mortality across Europe with a strong significance has already been showcased (Healy 2003). The observed rise in the impacts of weather related hazards was attributed due to the increase in population exposure, with a potential contribution from global warming (Forzieri et al. 2017). One of the findings showcased that the disaster could affect two-thirds of the European population by the year 2100 compared with 5% during the year 1981–2010 (Forzieri et al. 2017). Additionally, the most vulnerable people that are the ones with precarious physical environment living conditions or in environments where there are dramatic physical modifications (Liverman 2013).

The objective of this work is to perform a detailed analysis of the climatology and temporal evolution of 74 climate indices for the European continent from 1950 to 2021 at a monthly scale. The patterns of variation and temporal-spatial distribution characteristics of global radiation, maximum and minimum temperature, precipitation, relative humidity, and sea level pressure extreme events are quantified. The special feature is that the climate indices for each individual country in Europe were evaluated with high spatial resolution and up-to-date data. The main objectives are: For each climate indices covering Europe at a monthly frequency from 1950 to 2021, three statistics are obtained: (i) correlation coefficient using Pearson, Kendall, and Spearman; (ii) trend nature and significance using the Mann–Kendall test, and (iii) magnitude of trend using Sen's slope estimator.

This study used the R platform's ‘ClimInd’ (https://cran.r-project.org/web/packages/ClimInd/index.html) and ‘scPDSI’ (https://cran.r-project.org/web/packages/scPDSI/index.html) package to compute 74 climate indices at monthly temporal frequencies for Europe. Figure 1 shows the list of climate indices selected. The selection is based on the possible monthly scale climate indices from available input weather data (global radiation, maximum and minimum temperature, precipitation, relative humidity, sea level pressure, and wind speed), literature review discussed in the introduction section and important priority sectors such as agriculture, health, water, disaster risk reductions, and tourism. The climate indices were divided into eight categories: drought (15), global radiation (1), multi-element (5), precipitation (16), relative humidity (1), sea level pressure (1), temperature (32), and wind speed (3).

For each climate index covering Europe at a monthly frequency from 1950 to 2021, the study obtained three statistics (Section 1 explained as main objectives). The correlation coefficient between different possible combinations of climate indices was computed using each of the Pearson, Kendall, and Spearman methods. Pearson is widely used to measure linear relationships whereas Kendall and Spearman are non-parametric measures. Considering the nature of different climate indices being analysed we employed all three correlation techniques, finally, the resultant value was selected based on the maximum of three methods to avoid any missing crucial information for future assessments based on thresholds of the correlation value. There are two types of tests for detecting the significant trends in climatological time series: parametric (data are independent and normally distributed) and non-parametric (data is independent). Mann–Kendall and Sen's slope estimator are both non-parametric approaches which are employed in this study to detect variability and long-term monotonic trends in climate indices. The Mann–Kendall test was used to assess whether the climate indices nature is increasing (positive) or decreasing (negative) over time, and trend direction is statistically significant or not. A p-level of < 0.05 (statistically significant) and >0.05 in both the increasing (positive) and decreasing (negative) trend was represented spatially taking serial correlation into consideration. The Sen's slope estimator was used to compute the climate indices percentage of change over each year for the 72 year period (1950–2021). The value would indicate for instance how much on average it has modified each year either positively or negatively (indicating direction and size of trend) by overcoming the gross data time series errors and outliers if any.

Z-value positive indicates increasing trend and negative indicates decreasing trend. Testing of the trends is done at the 0.05 significance level. |Z| > Z_1–0.05/2, the null hypothesis of no trend is rejected, a significant trend exists in the dataset. The value of Z_1–0.05/2 is obtained from the standard normal distribution table which is 1.96 (5% significance level).

The analysis was further disintegrated into each of the European countries specifically to analyze the nature and magnitude of the trend. The statistical output of this study is showcased in the Results section. The study highlights how each of the climate indices of high spatial resolution varies across monthly frequency for European countries and regions as a whole extent.

Based on the visualization attached as a part of Supplementary information figure S1, the results indicate the following: PHDI, scPDSI, and WPLM are within the normal range except regions of Croatia and Slovenia which indicate a low to moderate drought, certain parts of southern Russia close to Black sea which indicates moderate to severe drought, more moist region across the Eastern and Northern Europe. spi-1 indicates that the southern part of Spain, Portugal, and Italy is moderately wet during June, July, and August as a progression of bell shape curve. Mild drought has been observed in the southern part of Greece, Spain, and Portugal mostly prevailing in August through spi-3, 6 indices. The eastern part of Russia indicates an increase of mild wetness from spi-1,3,6,9,12, and 24 months. Similar conditions are confirmed with the SPEI related indices for various frequencies. bio20 reveals that the southern part of Europe receives more global radiation in comparison to the northern part with the highest in June and July. Combined indices hi indicates that the experience would be cold ∼–10 °C in the northern part of Europe, ∼0 °C at central Europe, and warm ∼10 °C in Southern Europe during the winter season. There are increases in temperature during spring from Southern Europe to Central Europe, spreading across the western and eastern parts, and further increasing to reach the maximum in Northern Europe in July ∼30 °C. The temperature descends in autumn. The ‘mi’ index is ∼10 days during the summer and autumn seasons, with the highest in Western France, Eastern Ukraine, Northern Ireland, and Northern and Central United Kingdom. Hargreaves method was used to obtain the PET (Pet Evapotransportation) indices, which increase gradually from Southern Europe towards Western Europe and then cover Eastern and Northern Europe. The highest could be visualized in July and the Southern and Southern Eastern Europe Spain, Portugal, Monaco, Italy, San Marino, Southern Russia, Southern Ukraine, Serbia, Greece, Bulgaria, Bosnia and Herzegovina, Moldova, Romania. The maximum it can go is up to 200 mm/month during that period. Due to limited spatial availability of wind data the UTCI cannot be computed and spatially visualized for the whole European region. The northern part of Sweden and Finland experiences a feel of −80 °C during winter, and Germany, Netherlands, France, and some region of Spain experiences a feel of 20 °C during summer. WCI on the other hand indicates a similar experience during summer but for winter it is −20 °C maximum. Spatial spreading across the regions is similar to that of UTCI. Southern Europe especially Spain, Portugal, Italy and Greece has dry periods as long as one month during summer and approximately half for the month during other seasons and the other half as wet days. The probability is more than 95% for this occurrence of the event. During autumn southern France, some regions of Italy, Slovenia, northern Norway, and San Marino experience precipitation of more than 50 mm for as long as 1.5 days. Each of the European countries receives more than 1 and 3 mm of precipitation for all the seasons with highest during the December (winter) and July (spring) ∼20 days, respectively. The maximum amount of precipitation received in any given month for the European region at a recorded location is 400 mm. Europe receives >10 days of 10 mm and >6 days of 20 mm precipitation mostly Italy, San Marino, Southern France, Slovenia, Croatia, northern banks of Norway, and Southern Russia. The highest recorded average precipitation for any given day is ∼80 mm in the month of November (summer) and cumulative of 5 days is ∼150 mm along the similar regions where Europe receives most 10 and 20 mm precipitation. The relative humidity indices are in the range of 40–90% with the maximum during winter and lowest during the summer season. Spatially Northern, Eastern and Western Europe receives more relative humidity in comparison to central and Southern Europe. Sea level pressure does not vary much across the European region but reduces by ∼200 hPa in Southern Russia (eastern Europe) near the Black Sea, Ukraine, and Moldova for all the seasons except winter. During winter Northern, Central and Eastern Europe receive a minimum air temperature <0 °C for a full month which further reduces in the succeeding months. Maximum consecutive summer days are visualized during the summer season in Southern and Southern Eastern Europe. Cold spell duration of >1.5 days is observed in central, eastern and southern eastern Europe during winter. During summer months there has been a maximum difference of ∼21 days for maximum temperatures above/below 17 °C across Europe. The difference between the mean and maximum daily maximum and minimum air temperature during that period is >15 and >35 °C, respectively. During the summer season Southern and South Eastern Europe have mean air temperatures > 4 °C for >600 sum of degree days. The highest mean of minimum, maximum and mean air temperature during summer is ∼20, ∼30, ∼25 °C and in winter is ∼–20, ∼–10, ∼–15 °C, respectively. Mean air temperature >5 °C during spring where the first span of 6 days and 10 days moves from Southern Europe (March) to Central and Eastern Europe (April), to Northern Europe (May). Tropical nights could be observed during July and August in the southern part of Spain, Russia and France, Ukraine and Italy. The number of days when maximum temperature >0 °C and minimum temperature <0 °C is >20 during winter and spring shifts from Southern and Eastern Europe to Northern Europe; maximum during April in Sweden, Norway, Finland, and Northern Russia. fg indicate average wind speeds are higher during the winter season also the southern part of Norway, Finland, the central and northern part of Germany and Netherlands experiences relatively higher wind speed ∼10 m/s prevailing for >10 days.

There has been a very high positive correlation between gtx, txn with gtn, tnn, tnx with gtx, wci, pet_hargreaves with hi, txn, txx with tnn and tnx, wci, utci with pet_hargreaves for all the months throughout the 72 year time series of climate indices. This indicates that the following indices have no/less impact on seasonality in their correlation. Both the compared variables of climate indices have a perfect linear/non-linear relationship and modification of one variable would consistently change another (upward slope). In other terms, throughout the year mean, minimum, maximum of minimum and maximum daily air temperature, heat index which is a combination of air temperature and relative humidity and wind chill index which is the lowering of body temperature linked with the passing flow of lower-temperature air, potential evapotranspiration computed by Hargreaves method and heat index, wind chill index and universal thermal climate index which is the reference condition of air temperature causing the same model response as actual condition across Europe is positively correlated with >0.8 value. The indices which show positive very high correlation seasonality connections for winter (DJF) are pet_hargreaves, gtx, txn, txx, wci with slp, gd4, gtx, gtg, hi, ntg, tnn, txn, txx, utci, wci, pet_hargreaves, slp, xtg with bio20, cfd, fd with id, mi with wci; for spring (MAM) are gtn, tnn, tnx with dd17; for summer (JJA) are csd, dd17, gd4, gtg, gtx, hi, pet_hargreaves, su, txn, wci with bio20, gtn, tnn, tnx with dd17, gtn, tnn, tnx with su, gtn, tnn, tnx with csd; for autumn (SON) are gd4, gtg, gtn, gtx, hi, ntg, pet_hargreaves, slp, tnn, tnx, txn, txx, utci, wci, xtg with bio20, gtx, pet_hargreaves, txx, utci, wci, xtg with slp. During the winter season changes in average sea level pressure impact the minimum daily air temperature indices, wind chill index, and potential evapotranspiration, changes in average global radiation impact the mean, maximum and minimum daily air temperature indices as well as the sum of degree days over 4 °C, heat index, wind chill index, potential evapotranspiration, universal thermal climate index, changes in wind chill index and number of days with daily maximum temperature <0 °C have a direct impact on the number of days and consecutive days with minimum daily temperature <0 °C. During the spring season changes in the difference between the days with maximum temperature >17 and <17 °C have a direct impact on the minimum daily air temperature indices. During the summer the changes in average global radiation impact the number of consecutive days with maximum temperature >25 °C, the difference between days with maximum temperature >17 and <17 °C, the sum of days with mean temperature >4 °C, the number of days with maximum temperature >25 °C, mean of daily mean and maximum temperature, heat index, potential evapotranspiration, minimum of maximum temperature, wind chill index, maximum number of days and consecutive summer days with maximum temperature >25 °C and difference days with maximum temperature >17 and <17 °C is impacted by change in minimum temperature indices. During the autumn season changes in average global radiation impact the sum of days with mean temperature >4 °C, mean of maximum, minimum, and mean daily temperature, heat index, minimum of daily mean temperature, potential evapotranspiration, average sea level pressure, minimum and maximum of minimum and maximum daily air temperature, universal thermal climate index, wind chill index, maximum of daily mean air temperature, change in average sea level pressure impacts the mean and maximum of maximum daily air temperature, potential evapotranspiration, universal thermal climate index, wind chill index, and maximum of daily mean air temperature. The very high positive correlation monthly climate indices which do not fall into the seasonality or yearly brackets are listed in Table 1. For different months most of the indices are a part of global radiation, multi-element, temperature, and sea level pressure indices except for the months of July and August which includes precipitation indices whereas August includes relative humidity indices. However, none of the drought related or wind speed related indices is part of the highly positively correlated indices for Europe.

The indices which show a negative very high correlation seasonality connections for winter (DJF) are hd17, id with bio20, gtx, txn, txx with cfd and fd, gtn, tnn, tnx with id; for spring (MAM) are dd17, gtx, txn, txx with cfd and fd; for autumn (SON) are fd, hd17 with bio20, gtx, txn, txx with cfd, gtx, txn, txx with fd. During the winter season, the change in global radiation will impact inversely to the daily mean air temperature <17 °C and the number of days with daily maximum air temperature <0 °C, changes in the maximum number of days and consecutive days with daily minimum air temperature <0 °C impacts inversely to the mean, minimum and maximum of maximum daily air temperature, changes in number of days with daily maximum temperature <0 °C impacts inversely to the mean, minimum and maximum of minimum daily air temperature. During spring season changes in the maximum number of days and consecutive days with daily minimum air temperature <0 °C will impact inversely the difference between the daily maximum air temperature >17 and <17 °C, mean, minimum and maximum daily maximum air temperature. During the autumn season, the average global radiation will impact inversely the number of days with a daily minimum temperature <0 °C and daily mean air temperature <17 °C, changes in the number of days and consecutive number of days with daily minimum temperature <0 °C impacts inversely the mean, minimum, and maximum of daily maximum air temperature. The very high negative correlation monthly climate indices which do not fall into the seasonality or yearly brackets are listed in Table 2. For different months most of the indices are a part of global radiation, multi-element, temperature, and sea level pressure indices except for the months of July and August which include precipitation indices whereas July, August, and September includes relative humidity indices. However, none of the drought related or wind speed related indices is part of the highly negatively correlated indices for Europe.

Analysis of the Mann–Kendall and Sen's slope estimator tests over 72 years of E-OBS data shows that for the European region there is an increasing trend in spi-3, spi-6, spi-9, spi-12 and spi-24 (drought indices), hi and pet (multi-element indices), d50mm, d95p, r10mm, r20mm, r95tot, r99tot, rx1day and sdii (precipitation indices), dd17, gd4, gtg, gtn, gtx, ntg, tn90p, tnn, tnx, tx90p, txn, txx, vwd, wsdi and xtg (temperature indices), whereas a decreasing trend in spei-12 and spei-24 (drought indices), rh (relative humidity indices), cfd, csdi, fd, hd17, id, tn10p, tx10p, vcd, and zcd (temperature indices), fg and fg6bft (wind speed indices) throughout all the months. The magnitude of change is per month for all the climate indices. The highest magnitude of change in increasing trend was recorded for gd4 which is in the range of 0.14–0.93 °C, lowest in January and highest in August with an average increase of 0.49 °C/month, followed by pet which is in the range of 0.01 to 0.15 mm/day, lowest in December and highest in July with an average increase of 0.07 mm/day, followed by tn90p which is in the range of 0.04–0.14%, lowest in February and highest in August with an average increase of 0.08%, followed by tx90p which is in the range of 0.02–0.10%, with lowest in September and highest in June with an an average increase of 0.06%, followed by dd17 which is in the range of 0 to 0.09 days, with lowest in January and highest in April with an average increase of 0.04 days, followed by rx1day which is in the range of 0.01–0.05 mm, with lowest in August and highest in October with an average increase of 0.02 mm, followed by ntg which is in the range of 0.01–0.05 °C, with lowest in September and highest in March with an average increase of 0.03 °C. The highest magnitude of change in decreasing trend was recorded for hd17 which is in the range of 0.29–1.03 °C, lowest in January and highest in July with an average decrease of 0.64 °C, followed by rh which is in the range of 0–0.11%, lowest in October and highest in April with an average decrease of 0.05%, followed by tn10p which is in the range of 0.01–0.10%, lowest in February and highest in July with an average decrease of 0.05%. The percentage of change in the trend of climate indices is close to 0 in the rest of the indices throughout all the months for both the increasing and decreasing trends.

Climate indices are essential for analyzing the change and influence of climate on various sectors of society to provide stakeholders with information on the state of the climate. The seasonal trends of climate indices indicated a considerable change across seasons, indicating the necessity to undertake climate research at diverse time scales in order to understand the causes and implications of climate change (Peña-Angulo et al. 2020). In this study, efforts are made to calculate a series of monthly climate indices by using long-term weather datasets with high resolution and European regional coverage with information of real-world representation; direction and magnitude are also analyzed. Most studies have been conducted with the objective of quantifying the extremes through studies of temperature and precipitation climate indices or focusing on the 27 core extreme climate indices (Peterson et al. 2001). Other, less common climate indices have a significant impact on agriculture, environmental/ecological, hydrology, health, tourism and recreation.

The result and analysis of the study related to the monthly correlation of 74 climate indices in Europe during the period of 1950–2021 indicate that using Pearson, Kendall, and Spearman techniques for various climate indices showcases that Pearson had a marginal higher difference followed by Kendall and Spearman in comparison. The high positive (>0.80) and negative correlation (<–0.80) throughout the year, various seasons and months indicate the significance of each of the indices (not derived from similar weather datasets). Other indices than temperature and precipitation which are repetitive for most of the seasons and months include global radiation, heat index, mould index, potential evapotranspiration, sea level pressure and wind chill index. Potential Evapotranspiration has importance not only during the summer months but also during winter as it has a high correlation with global radiation and sea level pressure. Hence, computation and selection of potential evapotranspiration method plays a significant role. For summer months temperature difference below and above 17 °C is of major relevance to the minimum and maximum temperature derived temperature indices. Some of the temperature derived indices and precipitation indices are highly negatively correlated for the summer months which is quite obvious. Temperature indices computed based on temperature <0 °C (cold) are also highly negatively correlated with the global radiation and temperature derived indices using minimum and maximum air temperature for winter, spring and autumn. None of the drought based indices shows a strong correlation with any of the other climate indices. The importance of the correlation as a strength of the association between climatic variables is a key to confirming that not all climate indices follow the annuity or seasonality (monthly patterns of winter (DJF), spring (MAM), summer (JJA), autumn (SON)). So usage in climate research with an assumption and division based on seasonality and annual computation of the following indices might hide the ground reality.

It is essential to realise that the long-term climate history has inherent uncertainties that exhibit a rapid trend reversal. However, previous research has indicated a similar conclusion based on various indices such as in winter, significant rising precipitation trends during the 20th century dominate both average precipitation intensity and moderately intense occurrences, length of dry spells grew insignificantly, summer precipitation indices show few significant changes, negligible drying trends over Scandinavia, wetting trends over Central and Western Europe from 1921 to 1999, length of summer dry periods rose insignificantly, temperature distribution warm and cold tails warmed, significantly low cold tail values for maximum and minimum temperature in early 1940, winter warming from 1946 to 1999, summer warming is more in the first half of the century (Moberg & Jones 2005). In the European region from 1979 to 2017, temperature indices demonstrate that cold days and nights had negative trends, whereas warm days and nights had a positive trend, the precipitation indices showed a positive trend in Northern and Central Europe, whereas negative trend in Western Europe (Peña-Angulo et al. 2020). For European regions in the future significant rise in the summer heat, Mediterranean regions were vulnerable, positive trend in spring and fall, cold evenings will be less common, the frequency of warm days decrease, cold periods will be less frequent and intense with a significant fall in Northern Europe, heavy precipitation days to rise in central and northern Europe, a decrease in Mediterranean locations, increase in midsummer heavy precipitation days, summer and autumn with fewer wet days but higher rainfall volumes, Baltic nations and northern regions with increase in number of rainy days, changes in drought patterns, dryness over the Mediterranean, droughts will become less common in Europe (Cardell et al. 2020). Because the 1950s were a relatively dry decade in Central Europe, drought trends estimated for 1951–2015 are similar in direction but less in magnitude in comparison to 10 years shorter period 1961–2015, drying tendencies were seen in spring but were less prominent in summer, whereas wetting trends were observed in fall and winter, drought and high precipitation increase occurred concurrently at multiple locations in spring (Hänsel et al. 2019). For Western Europe from 1851 to 2018, spi-3 with a negative trend in summer, a decrease in severe drought during winter, spi-12 magnitude trends showed spatial differences with a positive trend in North France, Germany, British and Irish Isles, negative trends in Southeast of Iberian Peninsula, Balkans, and Italy, long-term drought patterns over Western Europe are statistically non-significant (Vicente-Serrano et al. 2021). For the European region trend analysis indicates spei-12 and scPDSI, most nations in Mediterranean and Central Europe show a substantial decreasing trend from 1902–2019, whereas countries in Northern Europe show positive trends, spi-12 follow the precipitation trend decrease in frequency from 2001–2019, frequency of drought has increased in Central Europe and Mediterranean in 2001–2019 (Ionita & Nagavciuc 2021). Wind indices displayed a negative trend in the north not statistically significant in the majority of the European region and a positive trend in the south particularly in spring and summer (Peña-Angulo et al. 2020). Since 1979, bioclimatic indices such as utci have increased by 1 °C indicating an increase in heat stress for Europe (Di Napoli et al. 2018). Bioclimatic indices indicate a cold-related stress decline compared to 1970 in colder European cities and Northern Europe with more decline, heat stress indicates an upward trend from 1976 to 2018 in all cities (Founda et al. 2019).

The visualization indicates that the standard precipitation index (spi) and standard precipitation evapotranspiration index (spei) are mostly of similar types of drought categories and can be used as an alternative during the unavailability of data to indicate the drought or wet conditions for Europe. However, drought values of spei are lower than spi. Combined indices – mi indicates it is not just temperature or relative humidity but the combined effect of temperature and relative humidity which is prevalent in the European region. utci indicates a colder experience than the minimum air temperature during winter and for summer it feels less warm than the maximum air temperature. wci indicates an approximately similar minimum and maximum air temperature. This indicates that the relative humidity and global radiation are prevailing in Europe which makes it significant to have a warmer or colder body experience than the normal temperature. Not much variation is observed among seasonality in drought indices PHDI, scPDSI and WPLM, the significant change is relatively low in Northern, Eastern, and Southeastern Europe with increasing trend and Southern, Western Europe with decreasing trend. A similar trend is observed in spi and spei with various frequencies of months 1, 3, 6, 9, 12, and 24. The drought indices with an increase in the frequency of months indicate a more increasing trend in the European regions discussed above. The heat index across Eastern Europe is increasing across all the months but for Ukraine from January to May, July and November it is decreasing, ice days decrease over the period of winter and spring in Northern, Eastern, Southeastern Europe. Increasing precipitation (prcptot, rx1day, rx5d, sdii) direction and magnitude in Northern Europe during winter however longer drier periods were observed resulting in a focus on compound analysis for such effects. Ukraine shows maximum variance along the seasons for relative humidity and southern Russia for sea level pressure. Cold days (cfd, fd) tend to decrease for the winter season whereas warmer days tend to increase for the summer season in Central and Eastern Europe. Mean, minimum and maximum air temperature increases, whereas mean temperature <17 °C decreases significantly across most zones of the European region spatially. Difference between maximum and minimum air temperature (dtr,etr,vdtr) during winter shows decreasing trend with ∼0.1 °C magnitude of change negatively for Northern, Central (some regions) and Eastern Europe, but positively for Southern and Western Europe.

Mann–Kendall and Sen's slope test indicates there has been an increasing trend in standard precipitation index at 3, 6, 9, 12, and 24 months, potential evapotranspiration, heat index, precipitation derived indices such as days more than 1 mm/total days, 10 mm, 20 mm, 50 mm, 95 percentile, total, 95 percentile/total, 99 percentile/total of precipitation. Warm, very warm days, warm spell duration and nights are of increasing nature. The increasing trend of warmer temperatures is counterbalanced by the decreasing trend in frost days, very cold days, zero crossing days, and cold spell duration. Standard precipitation evapotranspiration indices 12 and 24 show a decreasing trend which contradicts the spi behaviour. Wind speed and days with wind speed >10.8 m/s, relative humidity shows a decreasing trend. Global radiation, sea level pressure, universal thermal climate index, wind chill index, wet days of >1 mm, spi and spei for time-scale of 1 month show an increasing trend; whereas dry days, longest dry periods, diurnal temperature range, and extreme temperature range show a decreasing trend in the winter season. Summer days, diurnal temperature range, tropical nights, extreme temperature range, calm days show an increasing trend; whereas precipitation ≥3 mm, growing season 6 days with >5 °C, drought indices such as PHDI, scPDSI, WPLM, spei 1,3,6,9 months shows a decreasing trend in the summer season. Maximum percentage change of magnitude increasing trend has been observed in the sum of days with mean air temperature >4 °C at 0.93 °C (August), calm days with average wind speed ≤ 2 m/s at 0.28 days (June), total precipitation and days of total precipitation ≥1 mm at 0.18 mm (October), universal thermal climate index at 0.15 indice value (July), potential evapotranspiration at 0.14 mm/day (July), global radiation at 0.12 W/m² (April) and decreasing trend has been observed in heating degree days with mean air temperature <17 at 1.03 °C (January), calm days with average wind speed ≤ 2 m/s at 0.69 days (July), global radiation at 0.13 W/m² (May), relative humidity at 0.11% (December) across Europe.

European countries which would experience the highest possible change of climate indices increasing trend with >1% change and p-value < 0.05 of gd4 during August are Bosnia and Herzegovina (1.28 °C), Slovakia, San Marino, Croatia, Czechia, Montenegro, Slovenia, Republic of Serbia, Italy, Austria, Hungary, France, Malta, Andorra, Moldova, Poland, Luxembourg, Belgium, Portugal, Spain, Romania and Albania (1 °C), decreasing trend with >1% change and p-value < 0.05 of hd17 during January are Belarus (1.60 °C), Latvia, Estonia, Ukraine, San Marino, Slovenia, Croatia, Hungary, Poland, Montenegro, Slovakia, Republic of Serbia, Denmark, Czechia, Andorra, Iceland, Germany, Belgium, and Netherlands (1 °C). These countries experienced more warmer days during August and fewer colder days during January. Liechtenstein undergoes lower wind speed by 7 days during June and higher wind speed by 21 days during July. The universal thermal climate index of Liechtenstein increased by 3.88 indices value during July. High wind and high thermal climate indices were experienced by the Liechtenstein region during July. San Marino experienced warmer days during June by 1.39 °C. Portugal experienced more wind chill index of 0.72 indice value during May. Belarus experienced warmer days by 2.13 °C during March, Russia with a radiation decline of 1.92 W/m² during May, and Moldova with relative humidity by 1.07% during September. The indices which show a high percentage of change in both direction and magnitude are bio20, fgcalm, gd4, hd17, and utci. The indices which show a significant increase but a relatively low decrease in direction and magnitude of change are gd4, tn90p, and utci whereas the indices which show a significant decrease but relatively low increase in direction and magnitude of change are hd17 and rh.

To summarize, climate indices must be distinguished for each region according to whether they follow annual, seasonal, or monthly trends. Spatial patterns of climate indices indicate differences between annual, seasons and months for some of the analyzed climate indices. Furthermore, indices which follow a trend require a significance level check. If there is an increase or decrease in trend or magnitude of change for Europe overall it would still undergo bisection of country by country to add more relevant information for the performance of the indices. Various correlations would also add information about the inter-dependability of the various climate indices which might highlight if further multivariate analysis needs to be carried out. We already acknowledged the importance of potential evapotranspiration (pet) and in future, spatiotemporal trend analysis of climate indices that requires potential evapotranspiration for computation could be assessed with different evapotranspiration methods. Wind speed related indices are also of high importance but due to limited spatial data availability at higher resolution, it creates a void of trend assessment. The study can be utilized to define the recent climatic conditions of Europe and also the specific countries could focus on their climate relevant framework based on the trend of the climate indices. In future, multivariate techniques like EOFs could be used to identify the spatial patterns of climate variability and dominant patterns of variability in a dataset for the highly correlated positive and negative climate indices. Further investigations would be required to understand how true it is to the ground reality based on the stakeholder experiences.

The most important findings from this study are summarized as follows:

• •

  Climate indices indicate a monthly variability and the selection of any studies based on seasonality or annually leads to more uncertainty in assessments.

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  Climate indices correlation values could be useful for determining the month, seasonal and annual compound event pairs that are highly correlated either positively or negatively.

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  Climate index showing the highest increasing (decreasing) trend during all months is gd4 (hd17) across Europe. Maximum increase of +0.93 °C in August (gd4) and decrease of −1.03 °C in July (hd17).

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  Country-specific data reveals more information such as climate indices abrupt magnitude of change/month and experiences due to increasing or decreasing trends over a specific European region.

• • (a)

    Increasing trend results in fewer windy days (fgcalm – 7 days, June) and experience warmer (utci – 3.88 indice value, July) in Liechtenstein, warmer (gd4 – 1.39 °C, June) in San Marino, high wind speed and temperature (wci – 0.72 indice value, May) in Portugal.

  • (b)

    Decreasing trend results in high windy days (fgcalm – 21 days, July) in Liechtenstein, more warmer days (hd17 – 2.13 °C, March) in Belarus, less radiation (bio20 – 1.92 W/m², May) in Russia, lower relative humidity (rh – 1.07%, September), Moldova.

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  The analysis is not limited to the hotspot regions but also the visualization of nearby countries that can result in abrupt change over 72 years through various climate indices, which could be important for the stakeholder from a decision-making and hazard control perspective in response to shifting trends.

We acknowledge the E-OBS dataset from the EU-FP6 project UERRA (https://www.uerra.eu) and the Copernicus Climate Change Service, and the data providers in the ECA&D project (https://www.ecad.eu).

S.C.D., G.L., N.R., and M.I. conceptualized the study. S.C.D. and M.I. acquired data; S.C.D. and M.I. prepared the methodology. S.C.D. did analysis; S.C.D. did programming. G.L., N.R., M.I. did project administration; G.L., N.R., and M.I. supervised the study,S.C.D. wrote the original draft;S.C.D., G.L., N.R., and M.I. reviewed the article.

All relevant data are included in the paper or its Supplementary Information. Download input data (steps), code used for the statistical analysis (R and matlab) and output data generated from: https://zenodo.org/record/8250835.

The authors declare there is no conflict.