This statistic presents the perceived changes in annual global temperatures in the last 18 years, in selected European Countries in 2018. According to data published by Ipsos, the average guess among respondents in these countries was between 7 to 13 years, compared to the actual figure of **.

Hourly geographically aggregated weather data for Europe. This data package contains radiation and temperature data, at hourly resolution, for Europe, aggregated by Renewables.ninja from the NASA MERRA-2 reanalysis. It covers the European countries using a population-weighted mean across all MERRA-2 grid cells within the given country.

This dataset provides values for TEMPERATURE reported in several countries. The data includes current values, previous releases, historical highs and record lows, release frequency, reported unit and currency.

By the year 2050, the temperature of the warmest month in Ljublijana is expected to increase by eight degrees Celsius. This is the biggest increase when compared to the other European Union capital cities. The capital cities of both France and Germany are expected to see temperatures rise by *** degrees Celsius. With temperatures expected to increase, the possibility of wildfires increases. As of May 2019, countries such as France and Spain had suffered far more wildfires than the 2008 to 2018 average.

Bias-adjusted daily time series of mean, minimum (Tn) and maximum (Tx) temperature, and precipitation (Pr) for the period 1981–2100 for an ensemble of Regional Climate Models (RCMs) from EURO-CORDEX. RCMs are used to downscale the results of Global Climate Models from the Coupled Model Intercomparison Project Phase 5. All RCMs are run over the same numerical domain covering the European continent at a resolution of 0.11°. Historical runs, forced by observed natural and anthropogenic atmospheric composition, cover the period from 1950 to 2005; the projections (2006–2100) are forced by two Representative Concentration Pathways (RCP), namely, RCP4.5 and RCP8.5. RCMs’ outputs have been bias-adjusted using the methodology described in e.g. Dosio and Paruolo (2011) using the observational data set EOBSv10, and applied to the EURO-CORDEX data by Dosio (2016) and Dosio and Fischer (2018)

For further information the readers are referred to the following publications: Dosio, A., Fischer, E. M. (2018). Will Half a Degree Make a Difference? Robust Projections of Indices of Mean and Extreme Climate in Europe Under 1.5°C, 2°C, and 3°C Global Warming. Geophysical Research Letters, 45(2), 935–944. https://doi.org/10.1002/2017GL076222 Dosio, A. (2016). Projections of climate change indices of temperature and precipitation from an ensemble of bias-adjusted high-resolution EURO-CORDEX regional climate models. Journal of Geophysical Research: Atmospheres, 121(10), 5488–5511. https://doi.org/10.1002/2015JD024411 Dosio, A., Paruolo, P. (2011). Bias correction of the ENSEMBLES high-resolution climate change projections for use by impact models: Evaluation on the present climate. Journal of Geophysical Research, 116(D16), 1–22. https://doi.org/10.1029/2011JD015934

(From http://www.worldclim.org/methods) - For a complete description, see:

Hijmans, R.J., S.E. Cameron, J.L. Parra, P.G. Jones and A. Jarvis, 2005. Very high resolution interpolated climate surfaces for global land areas. International Journal of Climatology 25: 1965-1978.

The data layers were generated through interpolation of average monthly climate data from weather stations on a 30 arc-second resolution grid (often referred to as 1 km2 resolution). Variables included are monthly total precipitation, and monthly mean, minimum and maximum temperature, and 19 derived bioclimatic variables.

The WorldClim interpolated climate layers were made using: * Major climate databases compiled by the Global Historical Climatology Network (GHCN), the FAO, the WMO, the International Center for Tropical Agriculture (CIAT), R-HYdronet, and a number of additional minor databases for Australia, New Zealand, the Nordic European Countries, Ecuador, Peru, Bolivia, among others. * The SRTM elevation database (aggregeated to 30 arc-seconds, 1 km) * The ANUSPLIN software. ANUSPLIN is a program for interpolating noisy multi-variate data using thin plate smoothing splines. We used latitude, longitude, and elevation as independent variables.

Based on current monthly figures, on average, German climate has gotten a bit warmer. The average temperature for January 2025 was recorded at around 2 degrees Celsius, compared to 1.5 degrees a year before. In the broader context of climate change, average monthly temperatures are indicative of where the national climate is headed and whether attempts to control global warming are successful. Summer and winter Average summer temperature in Germany fluctuated in recent years, generally between 18 to 19 degrees Celsius. The season remains generally warm, and while there may not be as many hot and sunny days as in other parts of Europe, heat waves have occurred. In fact, 2023 saw 11.5 days with a temperature of at least 30 degrees, though this was a decrease compared to the year before. Meanwhile, average winter temperatures also fluctuated, but were higher in recent years, rising over four degrees on average in 2024. Figures remained in the above zero range since 2011. Numbers therefore suggest that German winters are becoming warmer, even if individual regions experiencing colder sub-zero snaps or even more snowfall may disagree. Rain, rain, go away Average monthly precipitation varied depending on the season, though sometimes figures from different times of the year were comparable. In 2024, the average monthly precipitation was highest in May and September, although rainfalls might increase in October and November with the beginning of the cold season. In the past, torrential rains have led to catastrophic flooding in Germany, with one of the most devastating being the flood of July 2021. Germany is not immune to the weather changing between two extremes, e.g. very warm spring months mostly without rain, when rain might be wished for, and then increased precipitation in other months where dry weather might be better, for example during planting and harvest seasons. Climate change remains on the agenda in all its far-reaching ways.

This dataset provides high-resolution gridded temperature and precipitation observations from a selection of sources. Additionally the dataset contains daily global average near-surface temperature anomalies. All fields are defined on either daily or monthly frequency. The datasets are regularly updated to incorporate recent observations. The included data sources are commonly known as GISTEMP, Berkeley Earth, CPC and CPC-CONUS, CHIRPS, IMERG, CMORPH, GPCC and CRU, where the abbreviations are explained below. These data have been constructed from high-quality analyses of meteorological station series and rain gauges around the world, and as such provide a reliable source for the analysis of weather extremes and climate trends. The regular update cycle makes these data suitable for a rapid study of recently occurred phenomena or events. The NASA Goddard Institute for Space Studies temperature analysis dataset (GISTEMP-v4) combines station data of the Global Historical Climatology Network (GHCN) with the Extended Reconstructed Sea Surface Temperature (ERSST) to construct a global temperature change estimate. The Berkeley Earth Foundation dataset (BERKEARTH) merges temperature records from 16 archives into a single coherent dataset. The NOAA Climate Prediction Center datasets (CPC and CPC-CONUS) define a suite of unified precipitation products with consistent quantity and improved quality by combining all information sources available at CPC and by taking advantage of the optimal interpolation (OI) objective analysis technique. The Climate Hazards Group InfraRed Precipitation with Station dataset (CHIRPS-v2) incorporates 0.05° resolution satellite imagery and in-situ station data to create gridded rainfall time series over the African continent, suitable for trend analysis and seasonal drought monitoring. The Integrated Multi-satellitE Retrievals dataset (IMERG) by NASA uses an algorithm to intercalibrate, merge, and interpolate “all'' satellite microwave precipitation estimates, together with microwave-calibrated infrared (IR) satellite estimates, precipitation gauge analyses, and potentially other precipitation estimators over the entire globe at fine time and space scales for the Tropical Rainfall Measuring Mission (TRMM) and its successor, Global Precipitation Measurement (GPM) satellite-based precipitation products. The Climate Prediction Center morphing technique dataset (CMORPH) by NOAA has been created using precipitation estimates that have been derived from low orbiter satellite microwave observations exclusively. Then, geostationary IR data are used as a means to transport the microwave-derived precipitation features during periods when microwave data are not available at a location. The Global Precipitation Climatology Centre dataset (GPCC) is a centennial product of monthly global land-surface precipitation based on the ~80,000 stations world-wide that feature record durations of 10 years or longer. The data coverage per month varies from ~6,000 (before 1900) to more than 50,000 stations. The Climatic Research Unit dataset (CRU v4) features an improved interpolation process, which delivers full traceability back to station measurements. The station measurements of temperature and precipitation are public, as well as the gridded dataset and national averages for each country. Cross-validation was performed at a station level, and the results have been published as a guide to the accuracy of the interpolation. This catalogue entry complements the E-OBS record in many aspects, as it intends to provide high-resolution gridded meteorological observations at a global rather than continental scale. These data may be suitable as a baseline for model comparisons or extreme event analysis in the CMIP5 and CMIP6 dataset.

The harmonization of data granularity in spatial and temporal terms is an important pre-step to any econometric and machine learning applications. Researchers, who wish to statistically test hypotheses on the relationship between agro-meteorological and economic outcomes, often observe that agro-meteorological data is typically stored in gridded and temporally detailed form, while many relevant economic outcomes are only available on an aggregated level. This dataset intends to aid empirical investigations by providing a dataset with monthly meteorological indicators on a European NUTS 3 regional level for 13 countries for the period from 1989 to 2018.

We created this dataset from daily data in a grid of 25km x 25km provided by the Joint Research Centre of the European Commission. We matched the map with the raw data to a map with the administrative boundaries of European NUTS3 regions. After appropriately weighting, we calculated the monthly, regional mean, variance and kurtosis of the following variables: daily maximum, minimum, average air temperature in degrees Centigrade, sum of precipitation in mm per day and snow depth in cm. We report the covariance between the average temperature and the precipitation as well.

This dataset provides a series of climate indices derived from reanalysis and model simulations data hosted on the Copernicus Climate Data Store (CDS). These indicators describe how climate variability and change of essential climate variables can impact sectors such as health, agriculture, forestry, energy, tourism, or water and coastal management. Those indices are relevant for adaptation planning at the European and national level and their development was driven by the European Environment Agency (EEA) to address informational needs of climate change adaptation national initiatives across the EU and partner countries as expressed by user requirements and stakeholder consultation. The indices cover the hazard categories introduced by the IPCC and the European Topic Centre on Climate Change Impacts, Vulnerability and Adaptation (ETC-CCA). They are also made available interactively through CDS Toolbox public visualisation apps on the European Climate Data Explorer hosted on EEA’s Climate-adapt site. The indices are either downloaded from the CDS where available, or calculated through a specific CDS Toolbox workflow. In this way both the calculations and the resulting data are fully traceable. As they come from different datasets the underlying climate data differ in their technical specification (type and number of climate and impact models involved, bias-corrected or not, periods covered etc.). An effort was made in the dataset selection to limit the heterogeneity of the underlying dataset as ideally the indices should come from the same dataset with identical specifications. The indices related to temperature, precipitation and wind (20 out of 30) were calculated from atmospheric variables in the same datasets: 'Climate and energy indicators for Europe from 2005 to 2100 derived from climate projections', and 'ERA5 hourly data on single levels from 1940 to present'. The other indices are directly available from CDS datasets generated by specific theme projects. More information about this dataset can be found in the documentation. The underlying datasets hosted on the CDS are:

ERA5 hourly data on single levels from 1940 to present - used to calculate most of the temperature, precipitation and wind speed indicators as it provides the historical and observation based baseline used to monitor the indicators. Climate and energy indicators for Europe from 2005 to 2100 derived from climate projections - used to calculate most of the temperature, precipitation and wind speed indicators as it provides bias-corrected sub-daily data. It is used for all the indicators except those specified in the following datasets below. Fire danger indicators for Europe from 1970 to 2098 derived from climate projections - provides the high fire danger days and fire weather indicators. Hydrology-related climate impact indicators from 1970 to 2100 derived from bias adjusted European climate projections - provides the river flood, river discharge, aridity actual, and mean soil moisture indicators. Mountain tourism meteorological and snow indicators for Europe from 1950 to 2100 derived from reanalysis and climate projections - provides the snowfall amount index. Water level change indicators for the European coast from 1977 to 2100 derived from climate projections - provides the relative sea level rise and extreme sea level indicators.

This dataset was produced on behalf of the Copernicus Climate Change Service.

In 2022, Bosnia and Herzegovina ranked first among the countries in Central and Eastern Europe (CEE) by mean temperature anomalies compared to the average from 1991 to 2021, which stood at 1.23 degrees Celsius. Serbia and Croatia followed with 1.21 and 1.19 degrees Celsius above the baseline, respectively. The lowest anomaly was recorded in Belarus, where the temperature departed from the average norm by about 0.5 degrees Celsius.

This dataset consists of a global daily analysis of surface air temperature for the whole Earth since 1850, based on combined information from satellite and in situ data sources, including uncertainty estimates. This is v1.0 of the product, which has been compiled as part of the European Union Horizon 2020 EUSTACE (EU Surface Temperature for All Corners of Earth) project.

This product provides global mean air temperature data on a regular lat-lon grid with a grid spacing of 0.25 degrees, and provides daily data from 1850 to 2015. Uncertainty estimates are also provided, with both a 'total' uncertainty, and an ensemble of 10 samples. The mean temperature data and uncertainty estimates provided are consistent across a broad range of space and time scales from daily 0.25° to multidecadal global averages. The coverage is significantly better than is available from station data alone, and covers land, ocean and ice areas.

This data has been derived using a statistical method to estimate air temperatures at all places and times. It takes into account uncertainty in the input data sets covering errors in the in situ measurements, land station homogenisation and errors in the air temperatures estimated from satellite data . Although the statistical model estimates temperatures at all locations, the product is not globally complete, as areas with too few data to provide a reliable air temperature estimate have been masked out.

(From http://www.worldclim.org/methods) - For a complete description, see:

Hijmans, R.J., S.E. Cameron, J.L. Parra, P.G. Jones and A. Jarvis, 2005. Very high resolution interpolated climate surfaces for global land areas. International Journal of Climatology 25: 1965-1978.

The data layers were generated through interpolation of average monthly climate data from weather stations on a 30 arc-second resolution grid (often referred to as 1 km2 resolution). Variables included are monthly total precipitation, and monthly mean, minimum and maximum temperature, and 19 derived bioclimatic variables.

The WorldClim interpolated climate layers were made using: * Major climate databases compiled by the Global Historical Climatology Network (GHCN), the FAO, the WMO, the International Center for Tropical Agriculture (CIAT), R-HYdronet, and a number of additional minor databases for Australia, New Zealand, the Nordic European Countries, Ecuador, Peru, Bolivia, among others. * The SRTM elevation database (aggregeated to 30 arc-seconds, 1 km) * The ANUSPLIN software. ANUSPLIN is a program for interpolating noisy multi-variate data using thin plate smoothing splines. We used latitude, longitude, and elevation as independent variables.

For the modelling of electricity production and demand, meteorological conditions are becoming more relevant due to the increasing contribution from renewable electricity production. But the requirements on meteorological data sets for electricity modelling are quite high. One challenge is the high temporal resolution, since a typical time step for modelling electricity production and demand is one hour. On the other side the European electricity market is highly connected, so that a pure country based modelling does not make sense and at least the whole European Union area has to be considered. Additionally, the spatial resolution of the data set must be able to represent the thermal conditions, which requires high spatial resolution at least in mountainous regions. All these requirements lead to huge data amounts for historic observations and even more for climate change projections for the whole 21st century. Thus, we have developed an aggregated European wide data set that has a temporal resolution of one hour, covers the whole EU area, has a reasonable size but is considering the high spatial variability. This meteorological data set for Europe for the historical period and climate change projections fulfills all relevant criteria for energy modelling. It has a hourly temporal resolution, considers local effects up to a spatial resolution of 1 km and has a suitable size, as all variables are aggregated to NUTS regions. Additionally meteorological information from wind speed and river run-off is directly converted into power productions, using state of the art methods and the current information on the location of power plants. Within the research project SECURES (https://www.secures.at/) this data set has been widely used for energy modelling.

The SECURES-Met dataset provides variables visible in the table.

SECURES-Met is available in a tabular csv format for the historical period (1981-2020, Hydro only until 2010) created from ERA5 and ERA5-Land and two future emission scenarios (RCP 4.5 and RCP 8.5, both 1951-2100, wind power starting from 1981, hydro power from 1971) created from one CMIP5 EUROCORDEX model (GCM: ICHEC-EC-EARTH, RCM: KNMI-RACMO22E, ensemble run: r12i1p1) on the spatial aggregation level

• NUTS0 (country-wide),
• NUTS2 (province-wide),
• NUTS3 (Austria only),
• and EEZ (Exclusive Economic Zones, offshore only).

The data is divided into the historical (Historical.zip) and the two emission scenarios (Future_RCP45.zip and Future_RCP85.zip), a README file, which describes, how the files are organized, and a folder (Meta.zip), which has information and shape files of the different NUTS levels. As population weighted temperature and radiation represent values in geographical areas more relevant for solar power, it is highly relevant to use population weighted files. Spatial mean should be used for reference only.

The project SECURES, in which this dataset was produced, was funded by the Climate and Energy Fund (Klima- und Energiefonds) under project number KR19AC0K17532.

SECURES-Energy

Weather-dependent renewable electricity systems are vulnerable to climate change impacts. Electricity generation and demand profiles considering weather and climate impacts are needed in energy system modelling. We present a consistent and high-quality energy database in data formats useful for energy system modelling and keeping the high spatiotemporal complexity of climate data. The open-access dataset SECURES-Energy contains all relevant electricity demand and supply components for the EU and several additional European countries in hourly resolution covering the period 1981-2100. It is based on reanalysis data ERA5(-Land) for the historical period and two EURO-CORDEX emission scenarios (RCP 4.5 and RCP 8.5). On the generation side, impacts on onshore and offshore wind power generation, solar PV generation, and hydropower generation (run-of-river and reservoirs) – which is often missing in comparable datasets – are provided. On the demand side, all demand components relevant to future electricity systems including e-heating, e-cooling, e-mobility, and electricity demand in industry, are provided.

The detailed methods are described in the final project report (see link below) in Chapter 2.2 and Chapter 4.3 and a related journal publication is currently in preparation.

Further information:

• Project website SECURES: https://www.secures.at/
• All project-related publications: https://www.secures.at/publications
• Final SECURES project report: https://www.secures.at/fileadmin/cmc/Final_Report_SECURES.pdf and https://www.klimafonds.gv.at/wp-content/uploads/sites/16/C061007-ACRP12-SECURES-KR19AC0K17532-EB.pdf

The SECURES-Energy dataset provides variables visible in the table.

1. Hourly profiles ERA5-Land 1981-2010
2. Hourly profiles RCP 4.5/RCP 8.5 2011-2100

Production profiles:

Demand profiles:

Electricity supply profiles for wind (onshore and offshore), hydro (run-of-river), and solar generation are provided for almost all European countries, namely: Andorra (AD), Albania (AL), Austria (AT), Bosnia and Herzegovina (BA), Belgium (BE), Bulgaria (BG), Switzerland (CH), Czech Republic (CZ), Germany (DE), Denmark (DK), Estonia (EE), Spain (ES), Finland (FI), France (FR), United Kingdom of Great Britain and Northern Ireland (GB), Greece (GR), Croatia (HR), Hungary (HU), Republic of Ireland (IE), Italy (IT), Liechtenstein (LI), Lithuania (LT), Luxembourg (LU), Latvia (LV), Montenegro (ME), North Macedonia (MK), Malta (MT), Netherlands (NL), Norway (NO), Poland (PL), Portugal (PT), Romania (RO), Serbia (RS), Sweden (SE), Slovenia (SI), Slovakia (SK), San Marino (SM), Ukraine (UA), Vatican (VA), and Kosovo (XK). The countries covered by the electricity demand profiles are the EU27 countries (except for Cyprus), CH, GB, and NO.

Industrial, heating, and cooling demand profiles are based on regressions developed in the H2020 Hotmaps project [1] [2].

SECURES-Energy is available in a tabular csv format for the historical period (1981-2010) created from ERA5 and ERA5-Land and two future emission scenarios (RCP 4.5 and RCP 8.5, both 2011-2100) created from one CMIP5 EURO-CORDEX model (GCM: ICHEC-EC-EARTH, RCM: KNMI-RACMO22E) on the spatial aggregation level NUTS0 (country-wide).

The data is divided into the historical (Historical.zip) and the two emission scenarios (Future_RCP45.zip and Future_RCP85.zip), a README file, which describes, how the files are organized, and a folder (Meta.zip), which has information and shapefiles of the different NUTS levels.

Hydro reservoir profiles are also published and can be found in the related dataset SECURES-Met: https://zenodo.org/records/7907883.

The project SECURES and corresponding publications are funded by the Climate and Energy Fund (Klima- und Energiefonds) under project number KR19AC0K17532.

[1] Fallahnejad M. Hotmaps-data-repository-structure 2019. https://wiki.hotmaps.eu/en/Hotmaps-open-data-repositories.

[2] Pezzutto S, Zambotti S, Croce S, Zambelli P, Garegnani G, Scaramuzzino C, et al. HOTMAPS - D2.3 WP2 Report –

(From http://www.worldclim.org/methods) - For a complete description, see:

Hijmans, R.J., S.E. Cameron, J.L. Parra, P.G. Jones and A. Jarvis, 2005. Very high resolution interpolated climate surfaces for global land areas. International Journal of Climatology 25: 1965-1978.

The data layers were generated through interpolation of average monthly climate data from weather stations on a 30 arc-second resolution grid (often referred to as 1 km2 resolution). Variables included are monthly total precipitation, and monthly mean, minimum and maximum temperature, and 19 derived bioclimatic variables.

The WorldClim interpolated climate layers were made using: * Major climate databases compiled by the Global Historical Climatology Network (GHCN), the FAO, the WMO, the International Center for Tropical Agriculture (CIAT), R-HYdronet, and a number of additional minor databases for Australia, New Zealand, the Nordic European Countries, Ecuador, Peru, Bolivia, among others. * The SRTM elevation database (aggregeated to 30 arc-seconds, 1 km) * The ANUSPLIN software. ANUSPLIN is a program for interpolating noisy multi-variate data using thin plate smoothing splines. We used latitude, longitude, and elevation as independent variables.

Dataset del covid-19 obtenido mediante t��cnicas de web Scrapping

ERA5 is the fifth generation ECMWF reanalysis for the global climate and weather for the past 8 decades. Data is available from 1940 onwards. ERA5 replaces the ERA-Interim reanalysis. Reanalysis combines model data with observations from across the world into a globally complete and consistent dataset using the laws of physics. This principle, called data assimilation, is based on the method used by numerical weather prediction centres, where every so many hours (12 hours at ECMWF) a previous forecast is combined with newly available observations in an optimal way to produce a new best estimate of the state of the atmosphere, called analysis, from which an updated, improved forecast is issued. Reanalysis works in the same way, but at reduced resolution to allow for the provision of a dataset spanning back several decades. Reanalysis does not have the constraint of issuing timely forecasts, so there is more time to collect observations, and when going further back in time, to allow for the ingestion of improved versions of the original observations, which all benefit the quality of the reanalysis product. ERA5 provides hourly estimates for a large number of atmospheric, ocean-wave and land-surface quantities. An uncertainty estimate is sampled by an underlying 10-member ensemble at three-hourly intervals. Ensemble mean and spread have been pre-computed for convenience. Such uncertainty estimates are closely related to the information content of the available observing system which has evolved considerably over time. They also indicate flow-dependent sensitive areas. To facilitate many climate applications, monthly-mean averages have been pre-calculated too, though monthly means are not available for the ensemble mean and spread. ERA5 is updated daily with a latency of about 5 days. In case that serious flaws are detected in this early release (called ERA5T), this data could be different from the final release 2 to 3 months later. In case that this occurs users are notified. The data set presented here is a regridded subset of the full ERA5 data set on native resolution. It is online on spinning disk, which should ensure fast and easy access. It should satisfy the requirements for most common applications. An overview of all ERA5 datasets can be found in this article. Information on access to ERA5 data on native resolution is provided in these guidelines. Data has been regridded to a regular lat-lon grid of 0.25 degrees for the reanalysis and 0.5 degrees for the uncertainty estimate (0.5 and 1 degree respectively for ocean waves). There are four main sub sets: hourly and monthly products, both on pressure levels (upper air fields) and single levels (atmospheric, ocean-wave and land surface quantities). The present entry is "ERA5 hourly data on single levels from 1940 to present".

Global daily-mean sea surface temperatures, presented on a 0.05° latitude-longitude grid, with gaps between available daily observations filled by statistical means, spanning late 1981 to recent time. Suitable for large-scale oceanographic meteorological and climatological applications, such as evaluating or constraining environmental models or case-studies of marine heat wave events. Includes temperature uncertainty information and auxiliary information about land-sea fraction and sea-ice coverage. For reference and citation see: www.nature.com/articles/s41597-019-0236-x.

(From http://www.worldclim.org/methods) - For a complete description, see:

Hijmans, R.J., S.E. Cameron, J.L. Parra, P.G. Jones and A. Jarvis, 2005. Very high resolution interpolated climate surfaces for global land areas. International Journal of Climatology 25: 1965-1978.

The data layers were generated through interpolation of average monthly climate data from weather stations on a 30 arc-second resolution grid (often referred to as 1 km2 resolution). Variables included are monthly total precipitation, and monthly mean, minimum and maximum temperature, and 19 derived bioclimatic variables.

The WorldClim interpolated climate layers were made using: * Major climate databases compiled by the Global Historical Climatology Network (GHCN), the FAO, the WMO, the International Center for Tropical Agriculture (CIAT), R-HYdronet, and a number of additional minor databases for Australia, New Zealand, the Nordic European Countries, Ecuador, Peru, Bolivia, among others. * The SRTM elevation database (aggregeated to 30 arc-seconds, 1 km) * The ANUSPLIN software. ANUSPLIN is a program for interpolating noisy multi-variate data using thin plate smoothing splines. We used latitude, longitude, and elevation as independent variables.