The amount of monthly hours of sunshine in England follows a similar pattern each year, with the longest durations occurring in Spring and Summer. During the period in consideration, the highest amount of monthly sunshine hours was recorded in May 2020, at over *** hours. This was more hours of sunlight than the UK average. Overcast and rainyIn addition to low periods of sunshine, England is also susceptible to precipitation. Between 2015 and 2023, the greatest number of days in which more than 1mm of rain fell was **** days in March of the latter year. The driest month was May 2020, with just *** rain days. Europe’s gloomiest and sunniest citiesThe United Kingdom has some of Europe's cloudiest cities, such as Glasgow, London and Manchester. On the other hand, most of Europe’s sunniest cities are located in Spain, with Alicante taking the lead at *** hours of monthly average sunshine.

In 2019, Belgium’s weather institute registered approximately 1,760 hours of sunshine. During the decade covering 2008 to 2019, 2018 turned out to be the sunniest year of them all. On average, 1,600 hours of sunshine per year were measured in Belgium over this period. However, the number of sunshine hours varied each year. For instance, Belgium reached its lowest level of sunshine in 2008 with less than 1,500 hours. On the other hand, the country experienced an average of 200 days of rainfall per year, during this period.

Europe comparison

Although the Netherlands and Germany neighbor Belgium and share the same climate, hours of sunshine were higher in both countries. For instance, in 2018, over two thousand hours of sunshine were measured in the Netherlands and Germany. Although these differences are not outstanding, sunshine measures were continually higher from 2008 to 2018. Belgium was, therefore, not the sunniest destination in Europe. However, the country was not the least sunny either. Fewer hours of sunshine were, for example, reported in the United Kingdom.

Vitamin D

Vitamin D, also referred to as the sun vitamin, is created from our body when exposed to the sun’s UVB radiations. These rays are mostly found during the summer months and, therefore, insufficient exposure to the sun can create a Vitamin D deficiency. This vitamin is linked to various health benefits and its deficiency to diverse health implications. A day-light deficit can favor seasonal depression, characterized by tiredness; especially in Belgium, which reported an irregular number of summer days from 2008 to 2018. In 2014, around 20 percent of the Belgians were consuming Vitamin D supplements.

In January 2025, Germany experienced an overall average of 61 sunshine hours, which was an increase compared to the previous month, despite it being winter. Sunshine hours are also referred to as sunshine duration. As can be seen on this graph, the amount that Germany receives differs by season, even quite starkly just by month. Sunniest states When looking at federal states in Germany in 2024, the sunniest states in summer were Berlin, Brandenburg and Saxony. Confirming popular opinion, Hamburg was indeed the state with less sunshine hours in recent years, though not the least sunny compared to others further down the list. In winter, based on recent figures, Germany counted 392 sunshine hours. These figures may change more in the coming years due to the effects of climate change on the weather all over the country. National weather service The German National Meteorological Service (Deutscher Wetterdienst or DWD) monitors the weather in Germany. The service is a federal authority providing information for the population and conducting scientific research. It is also responsible for issuing official warnings when weather conditions are predicted to be threatening.

Spain is a major European holiday destination. Besides its cultural and architectural appeal, the Mediterranean country draws in millions of tourists in search of the warm Spanish sun. This sunshine stays mainly in southern, coastal and insular areas of Spain, with Huelva topping the list of sunniest Spanish cities at over 3.2 thousand sunshine hours in 2018. Major coastal holiday destinations, such as Malaga, Almeria or Alicante also made the list, all of them with over three thousand hours of sunshine in 2018. In contrast, Bilbao ranked as the Spanish area with the lowest number of sunshine hours in 2017.

August: the driest and hottest month in Spain Most of that sunshine is concentrated in August, which also ranked as the hottest month in Spain in 2018. The Spanish mean temperature for the said month averaged out at 25.6 degrees, while the coldest month that year was February, with an average of 6.9 degrees Celsius. In 2017, the rainiest month was November, when over 111 millimeters of precipitation were registered on average in Spain. The driest month was again August, which recorded only an average of 18.2 millimeters of precipitation that year.

Tourism constitutes an essential industry for the Spanish economic system Travel and tourism have become one of the leading engines of growth for the Spanish economy, featuring an ongoing increase in the GDP contribution over the last years and projected to reach approximately 178 billion euros in 2018. Spain ranked second on the World Tourism Organization’s list of most visited countries in the world, with its number of international visitors amounting to nearly 82 million in 2017. The Mediterranean country is also one of Europe’s favorite holiday destinations in 2018 – the United Kingdom, Germany and France appeared in the leading positions of the largest number of international visitors to Spain by country of residence, as confirms the latest studies.

Heating degree day (HDD) index is a weather-based technical index designed to describe the need for the heating energy requirements of buildings. Cooling degree day (CDD) index is a weather-based technical index designed to describe the need for the cooling (air-conditioning) requirements of buildings.

HDD and CDD are derived from meteorological observations of air temperature, interpolated to regular grids at 25 km resolution for Europe. Calculated gridded HDD and CDD are aggregated and subsequently presented on NUTS-3 level.

This dataset includes monthly data as published by the Joint Research Centre's AGRI4CAST Resources Portal (Note that Eurostat is not the producer of the monthly data, but is only re-publishing them). Annual data are calculated as sum of monthly data by Eurostat.

A number of research activities on land surfaces, especially such funded by the European Commission, deal with the possible land degradation and desertification processes in the Mediterranean and the interference to the other regions of Europe. To cover the whole Mediterranean area and to include as much as practicable of the adjacent European land-surface, the region of interest for the 1-km data set is defined as:

55 N/10 W 55 N/42 E 27 N/10 W 27 N/42 E

This area is extended for summermonths up to 72° N.

The data set represents the daily AVHRR-Data of NOAA 11, 14, 16 and 18 as latitude-longitude-projection in 0.01 degree resolution.

The data are divided into tiles of 5*4 ° ( 10w_31n ...35e_51n), seperately for

1 / 2 = calibrated AVHRR-CH 1 (VIS) / CH 2 (NIR) 3 - 5 = calibrated AVHRR-CH 3 - 5 (IR) 6 / 7 = local satellite / sun zenith distance 8 / 9 = local satellite / sun azimuth a = TOA broad-band albedo i = origin indicator and bitmaps n = NDVI p = local scattering angle s = sea/land surface temperatures (split window) t = local time of observation T The data are 16-bit integers reflectance in 1/100 % NDVI in 1/100 % thermal channel in 1/100 ° Celsius angels in 1/100 ° time in 1/10 second bitmap BIT0 - BIT 5 origin indicator BIT0 - BIT3 relative date BIT4/5 offset to first orbit day/night BIT6 - BIT12 number of dekade BIT13 cloudy = 1 (Saunders/Kriebel) BIT14 cloudy = 1; BIT15 water = 1, land = 0

Areas resp. days not covered are filled with missing value.

The tiles of interest can be best found in the CERA WWW-Gateway by Find entries by name e.g. as MEDOKADS_00w_39n_n to get the NDVI for the tile 0 ° - 5 E, 35 - 39 N.

Credit to the original author: The dataset was originally published here

Weather prediction dataset

A dataset for teaching machine learning and deep learning

Hands-on teaching of modern machine learning and deep learning techniques heavily relies on the use of well-suited datasets. The "weather prediction dataset" is a novel tabular dataset that was specifically created for teaching machine learning and deep learning to an academic audience. The dataset contains intuitively accessible weather observations from 18 locations in Europe. It was designed to be suitable for a large variety of different training goals, many of which are not easily giving way to unrealistically high prediction accuracy. Teachers or instructors thus can chose the difficulty of the training goals and thereby match it with the respective learner audience or lesson objective. The compact size and complexity of the dataset make it possible to quickly train common machine learning and deep learning models on a standard laptop so that they can be used in live hands-on sessions.

The dataset can be found in the `\dataset` folder and be downloaded from zenodo: https://doi.org/10.5281/zenodo.4980359

References

If you make use of this dataset, in particular if this is in form of an academic contribution, then please cite the following two references:

• Klein Tank, A.M.G. and Coauthors, 2002. Daily dataset of 20th-century surface air temperature and precipitation series for the European Climate Assessment. Int. J. of Climatol., 22, 1441-1453. Data and metadata available at http://www.ecad.eu
• Florian Huber, Dafne van Kuppevelt, Peter Steinbach, Colin Sauze, Yang Liu, Berend Weel, "Will the sun shine? – An accessible dataset for teaching machine learning and deep learning", DOI TO BE ADDED!

Map of the locations of the 18 weather stations from which data was collected

A number of research activities on land surfaces, especially such funded by the European Commission, deal with the possible land degradation and desertification processes in the Mediterranean and the interference to the other regions of Europe. To cover the whole Mediterranean area and to include as much as practicable of the adjacent European land-surface, the region of interest for the 1-km data set is defined as:

55 N/10 W 55 N/42 E 27 N/10 W 27 N/42 E

This area is extended for summermonths up to 72° N.

The data set represents the daily AVHRR-Data of NOAA 11, 14, 16 and 18 as latitude-longitude-projection in 0.01 degree resolution.

The data are divided into tiles of 5*4 ° ( 10w_31n ...35e_51n), seperately for

1 / 2 = calibrated AVHRR-CH 1 (VIS) / CH 2 (NIR) 3 - 5 = calibrated AVHRR-CH 3 - 5 (IR) 6 / 7 = local satellite / sun zenith distance 8 / 9 = local satellite / sun azimuth a = TOA broad-band albedo i = origin indicator and bitmaps n = NDVI p = local scattering angle s = sea/land surface temperatures (split window) t = local time of observation T The data are 16-bit integers reflectance in 1/100 % NDVI in 1/100 % thermal channel in 1/100 ° Celsius angels in 1/100 ° time in 1/10 second bitmap BIT0 - BIT 5 origin indicator BIT0 - BIT3 relative date BIT4/5 offset to first orbit day/night BIT6 - BIT12 number of dekade BIT13 cloudy = 1 (Saunders/Kriebel) BIT14 cloudy = 1; BIT15 water = 1, land = 0

Areas resp. days not covered are filled with missing value.

The tiles of interest can be best found in the CERA WWW-Gateway by Find entries by name e.g. as MEDOKADS_00w_39n_n to get the NDVI for the tile 0 ° - 5 E, 35 - 39 N.

Species distribution models can predict the suitable climatic range of a potential biological control agent (BCA), but they provide little information on the BCA's potential impact. To predict high population build-up, a pre-requisite of impact, studies are needed which assess the effect of environmental factors on vital rates of a BCA across the environmental gradient of the BCA’s suitable habitats, especially for the region where the BCA is considered for field release. We extended a published species distribution model with climate-dependent vital rates of Ophraella communa, a recently and accidentally introduced potential BCA of common ragweed, Ambrosia artemisiifolia in Europe. In field and laboratory experiments, we collected data on climate-dependent parameters assumed to be the most relevant for the population build-up of O. communa, i.e. temperature driving the number of generations per year and relative humidity (RH) determining egg hatching success. We found that O. communa concluded one generation in 334 cumulative degree days, and that egg hatching success strongly decreased from >80% to <20% when RH drops from 55% to 45% during the day. We used these values to spatially explicitly project population densities across the European range suitable for both common ragweed and the beetle and found that the present distribution of the beetle in Europe is within the range with the highest projected population growth. The highest population density of O. communa was predicted for northern Italy and parts of western Russia and western Georgia. Field observations of high impact on common ragweed with records of 80% aerial pollen reduction in the Milano area since the establishment of O. communa are in line with these predictions. The relative importance of temperature and RH on the population density of O. communa varies considerably across its suitable range in Europe. We propose that the combined statistical and mechanistic approach outlined in this paper helps to more accurately predict the potential impact of a weed BCA than a species distribution model alone. Identifying the factors limiting the population build-up of a BCA across the suitable range allows implementation of more targeted release and management strategies to optimize biocontrol efficacy.

This links to a file called report.json which contains Agricultural Meteorology Data for the past 7 days for a number of synoptic weather stations. The file is updated daily. Notes on the table All data are averaged or summed over the 7 day period A blank entry means that data were not available Normal means 30 year means from 1981 to 2010 • Temp: Average Air Temperature and difference from normal in degrees C • Rain: Total rainfall in mm and % of normal • Sun: Total sunshine duration in hours and % of normal • Soil: Average 10cm soil temperature in degrees C and difference from normal • Wind: Average wind speed in knots and difference from normal • Radiation: Total solar radiation in Joules/cm2 and % of normal

A number of research activities on land surfaces, especially such funded by the European Commission, deal with the possible land degradation and desertification processes in the Mediterranean and the interference to the other regions of Europe. To cover the whole Mediterranean area and to include as much as practicable of the adjacent European land-surface, the region of interest for the 1-km data set is defined as:

55 N/10 W 55 N/42 E 27 N/10 W 27 N/42 E

This area is extended for summermonths up to 72° N.

The data set represents the daily AVHRR-Data of NOAA 11, 14, 16 and 18 as latitude-longitude-projection in 0.01 degree resolution.

The data are divided into tiles of 5*4 ° ( 10w_31n ...35e_51n), seperately for

1 / 2 = calibrated AVHRR-CH 1 (VIS) / CH 2 (NIR) 3 - 5 = calibrated AVHRR-CH 3 - 5 (IR) 6 / 7 = local satellite / sun zenith distance 8 / 9 = local satellite / sun azimuth a = TOA broad-band albedo i = origin indicator and bitmaps n = NDVI p = local scattering angle s = sea/land surface temperatures (split window) t = local time of observation T The data are 16-bit integers reflectance in 1/100 % NDVI in 1/100 % thermal channel in 1/100 ° Celsius angels in 1/100 ° time in 1/10 second bitmap BIT0 - BIT 5 origin indicator BIT0 - BIT3 relative date BIT4/5 offset to first orbit day/night BIT6 - BIT12 number of dekade BIT13 cloudy = 1 (Saunders/Kriebel) BIT14 cloudy = 1; BIT15 water = 1, land = 0

Areas resp. days not covered are filled with missing value.

The tiles of interest can be best found in the CERA WWW-Gateway by Find entries by name e.g. as MEDOKADS_00w_39n_n to get the NDVI for the tile 0 ° - 5 E, 35 - 39 N.

In the INDECIS project, around 610K meteorological station-based observations were rescued over the Balkans and Central Europe for the main climate variables (maximum and minimum temperature, rainfall, sunshine duration and snow depth) along the 20th century at daily scale. Digitizing was carried out by using a strict "key as you see" method, meaning that the digitizers type the values provided by data images, rather than using any coding system. Digitizers carefully cross-checked the typed values against original sources for the 10th, 20th and 30th day of each month to make sure that no days were skipped or repeated during the digitizing process. Monthly totals and statistical summaries were computed from transcribed data and were compared with monthly totals and summaries provided by data sources to check accuracy as preliminary quality control. This dataset is considered as raw data since any consistent quality control and homogenisation testing were applied to identify potential errors and data biases.

Context

The dataset featured below was created by reconciling measurements from requests of individual weather attributes provided by the European Climate Assessment (ECA). The measurements of this particular dataset were recorded by a weather station near Heathrow airport in London, UK.

-> This weather dataset is a great addition to this London Energy Dataset. You can join both datasets on the 'date' attribute, after some preprocessing, and perform some interesting data analytics regarding how energy consumption was impacted by the weather in London.

Content

The size for the file featured within this Kaggle dataset is shown below — along with a list of attributes and their description summaries: - london_weather.csv - 15341 observations x 10 attributes

1. date - recorded date of measurement - (int)
2. cloud_cover - cloud cover measurement in oktas - (float)
3. sunshine - sunshine measurement in hours (hrs) - (float)
4. global_radiation - irradiance measurement in Watt per square meter (W/m2) - (float)
5. max_temp - maximum temperature recorded in degrees Celsius (°C) - (float)
6. mean_temp - mean temperature in degrees Celsius (°C) - (float)
7. min_temp - minimum temperature recorded in degrees Celsius (°C) - (float)
8. precipitation - precipitation measurement in millimeters (mm) - (float)
9. pressure - pressure measurement in Pascals (Pa) - (float)
10. snow_depth - snow depth measurement in centimeters (cm) - (float)

Source

Weather Data - https://www.ecad.eu/dailydata/index.php

The Effective Atmospheric Angular Momentum (EAAM) is a combined project from the Met Office and the European Centre for Medium Range Weather Forecasting (ECMWF). The data is of 3 angular momentum components of the mass and wind terms at 12 or 24 hourly intervals. The ECMWF data are from 1979-93. The corresponding Met Office Unified Model data cover the period from 1983 to 1997. This dataset is public.

https://catalogue.ceda.ac.uk/uuid/bf626d5254cb9df807c3ffef170b2331

Regardless of whether the rain in Spain stays mainly in the plain, the truth is annual precipitations in the Mediterranean country experienced a downward trend in recent years, with around *** millimeters of rainfall recorded in 2023. Nevertheless, this figure increased in 2024. For instance, March – one of Spain's wettest months – registered just over *** millimeters of rain in 2024, up ** percent from the same month the previous year. However, the record high of *** millimeters was recorded in March 2018. Spain: Europe’s suntrapMany picture Spain as a dream summer holiday destination – Mediterranean cuisine in the form of tapas, great beaches, and what many visit the country for – its warm climate and sweet sunshine. This enthusiasm for the European country is then not too surprising, since most of its sunniest areas exceeded ***** hours of sunshine according to data provided by the Spanish Statistics Institute. Tourism constitutes an essential industry for the Spanish economic systemTravel and tourism have become one of the leading engines of growth for the Spanish economy, featuring an ongoing increase in the GDP contribution over the last years – despite a drop due to the COVID-19 pandemic – and is projected to reach nearly *** billion euros in 2025.

Three 3 m x 3 m quadrats were used to sample European buckthorn abundance, plant species richness, and ground temperature in three different plots at York University: Boyer woodlot, Osgoode woodlot, and Saywell woodlot. The quadrats were randomly within each woodlot. Coordinates of each quadrat were generated by MicrosoftExcel software with relation to a chosen corner of the woodlot. Distance was measured using a measuring wheel. Woodlot dimensions were calculated using MapDevelopers software. Number of European buckthorn plants in each quadrat was recorded as European buckthorn abundance, and number of plant species in each quadrat excluding European buckthorn was recorded as plant species richness. Plants species included moss (phylum Bryophyta), grass (phylum Tracheophyte), low-level plants, shrubs and trees. Any human activity proximal to the quadrats was measured, the ground temperature of a spot in each quadrat was measured (degrees Celsius), and the outside temperature (degree Celsius) on each day was measured. Sunlight coverage of each quadrat was estimated but not used as a factor in this lab report because a means of accurate measurement was not available. Replicates of these measurements were taken on three different days: October 1, October 8, and October 15, in order to eliminate variance due to changes in temperature and weather. These dates are referred to as week 1, week 2, and week 3.

Anthropogenic land use changes, such as deforestation and commercial forestry, have substantially reduced natural roost sites for European bats. A common conservation solution is to provide artificial roosts (i.e., bat boxes), but there are concerns that these can become hotter than natural roosts in summer and could be death traps during heat waves. Nevertheless, females of several bat species form maternity colonies in these boxes, thus occupying hotter and more humid microclimates than solitarily roosting males. We tested if cooling efficiency and heat tolerance differ between sexes in European bats, and estimated the evaporative water requirements of living in bat boxes during hot summer days. We used indirect calorimetry and thermometry to quantify thermoregulation at high air temperatures (Ta) in four species of verspitilionid bats that regularly occupy artificial roosts. We measured resting metabolic heat production, evaporative water loss rates (EWL), and body temperature (Tb) at Ta between 28 °C and 48 °C during summer. We predicted that females have higher evaporative cooling capacity (evaporative heat loss/metabolic heat production) than males, allowing them to reach their heat tolerance limit at higher Ta. We found no sex differences in maximum evaporative cooling efficiency, maximum Tb, and maximum Ta tolerated. However, the patterns of increasing EWL with Ta differed between sexes. Females tolerated higher Ta before increasing EWL than males and then rapidly increased EWL to higher values than males at the maximum Ta tolerated. These sex differences in heat‑dissipation strategies may reflect varying ecological and physiological constraints associated with different summer roosting habits. Our study revealed that small European bat species are already at risk of succumbing to lethal dehydration during present-day heat waves. For conservation managers working with common European bat species, particularly those in monoculture forests with woodcrete bat-boxes, our physiologically-informed recommendations include positioning boxes in diverse locations varying in aspect and sun exposure. This will ensure thermal heterogeneity of roost sites and provide a wide gradient of microclimate conditions, allowing for roost switching when necessary.

A number of research activities on land surfaces, especially such funded by the European Commission, deal with the possible land degradation and desertification processes in the Mediterranean and the interference to the other regions of Europe. To cover the whole Mediterranean area and to include as much as practicable of the adjacent European land-surface, the region of interest for the 1-km data set is defined as:

55 N/10 W 55 N/42 E 27 N/10 W 27 N/42 E

This area is extended for summermonths up to 72° N.

The data set represents the daily AVHRR-Data of NOAA 11, 14, 16 and 18 as latitude-longitude-projection in 0.01 degree resolution.

The data are divided into tiles of 5*4 ° ( 10w_31n ...35e_51n), seperately for

1 / 2 = calibrated AVHRR-CH 1 (VIS) / CH 2 (NIR) 3 - 5 = calibrated AVHRR-CH 3 - 5 (IR) 6 / 7 = local satellite / sun zenith distance 8 / 9 = local satellite / sun azimuth a = TOA broad-band albedo i = origin indicator and bitmaps n = NDVI p = local scattering angle s = sea/land surface temperatures (split window) t = local time of observation T The data are 16-bit integers reflectance in 1/100 % NDVI in 1/100 % thermal channel in 1/100 ° Celsius angels in 1/100 ° time in 1/10 second bitmap BIT0 - BIT 5 origin indicator BIT0 - BIT3 relative date BIT4/5 offset to first orbit day/night BIT6 - BIT12 number of dekade BIT13 cloudy = 1 (Saunders/Kriebel) BIT14 cloudy = 1; BIT15 water = 1, land = 0

Areas resp. days not covered are filled with missing value.

The tiles of interest can be best found in the CERA WWW-Gateway by Find entries by name e.g. as MEDOKADS_00w_39n_n to get the NDVI for the tile 0 ° - 5 E, 35 - 39 N.

A number of research activities on land surfaces, especially such funded by the European Commission, deal with the possible land degradation and desertification processes in the Mediterranean and the interference to the other regions of Europe. To cover the whole Mediterranean area and to include as much as practicable of the adjacent European land-surface, the region of interest for the 1-km data set is defined as:

55 N/10 W 55 N/42 E 27 N/10 W 27 N/42 E

This area is extended for summermonths up to 72° N.

The data set represents the daily AVHRR-Data of NOAA 11, 14, 16 and 18 as latitude-longitude-projection in 0.01 degree resolution.

The data are divided into tiles of 5*4 ° ( 10w_31n ...35e_51n), seperately for

1 / 2 = calibrated AVHRR-CH 1 (VIS) / CH 2 (NIR) 3 - 5 = calibrated AVHRR-CH 3 - 5 (IR) 6 / 7 = local satellite / sun zenith distance 8 / 9 = local satellite / sun azimuth a = TOA broad-band albedo i = origin indicator and bitmaps n = NDVI p = local scattering angle s = sea/land surface temperatures (split window) t = local time of observation T The data are 16-bit integers reflectance in 1/100 % NDVI in 1/100 % thermal channel in 1/100 ° Celsius angels in 1/100 ° time in 1/10 second bitmap BIT0 - BIT 5 origin indicator BIT0 - BIT3 relative date BIT4/5 offset to first orbit day/night BIT6 - BIT12 number of dekade BIT13 cloudy = 1 (Saunders/Kriebel) BIT14 cloudy = 1; BIT15 water = 1, land = 0

Areas resp. days not covered are filled with missing value.

The tiles of interest can be best found in the CERA WWW-Gateway by Find entries by name e.g. as MEDOKADS_00w_39n_n to get the NDVI for the tile 0 ° - 5 E, 35 - 39 N.

Mallorca is a very popular travel destination among Germans: on top of the approximately *** million tourist arrivals from other Spanish regions, approximately **** million visits from the central European country were registered by the island in 2023. With a wide margin, residents of the United Kingdom were the second-largest international group to enjoy the sun on the Spanish destination. Mallorca is the largest of the Balearic Islands, which constitute one of Spain’s 17 autonomous communities and are home to a population of over *** million people. Summer: the favorite season for tourism in the Balearics The Mediterranean spot is an attractive sunny retreat for many Europeans in search of some summer relaxation. In 2022, the Balearic Islands welcomed more than ** percent of its annual international inbound air passenger traffic between June and September, registering around *** million arrivals each month. International holidaymakers are key drivers of the tourism sector in the region: foreign tourists stayed on average *** days enjoying the islands’ great weather in 2022, a less than ***** percent decrease from the previous year. Similarly, the per capita daily expenditure of international travelers in the Balearic Islands decreased to *** euros in 2022. Tourism is key both for the Islands and for the country Tourism has grown to become one of the most important sectors not only for the Spanish community but for the entire country. After increasing steadily over the last years, the contribution of tourism to Spain’s GDP stood at approximately *** billion euros in 2022, being forecasted to reach over *** billion in 2023. This sector also boosted the Spanish employment market by experiencing a significant improvement in the number of people employed apart from the pandemic years.