This is particularly problematic in highly variable terrain with low station density 13. While interpolated datasets 8 often perform well in matching precipitation or temperature of the stations from which they are produced, they often fail to accurately predict patterns between stations. Climatologies based on satellites or statistical downscaling, considered superior to interpolated data 12 for ecological applications, are currently either not available on a global scale or are still too coarse to reflect the small scale patterns needed in ecological studies. The gap between these spatial scales may be bridged using satellite data (CHIRPS 1, TRMM 2, 3) and statistical downscaling 4– 7 for a specific region of interest and/or interpolation methods applied to meteorological station data (WorldClim 8, CRU 9, GPCC 10, PRISM 11). Whereas many studies in these fields are conducted at a resolution of ~1 km 2, state-of-the-art global climate reanalyses often only represent climatic variation at spatial resolutions of 0.25°–1° (ca. High-resolution climate data are essential to many applications in environmental and ecological sciences.

We further show that CHELSA climatological data has a similar accuracy as other products for temperature, but that its predictions of precipitation patterns are better. We compare the performance of the new climatologies in species distribution modelling and show that we can increase the accuracy of species range predictions. We compare the data derived from the CHELSA algorithm with other standard gridded products and station data from the Global Historical Climate Network.

The resulting data consist of a monthly temperature and precipitation climatology for the years 1979–2013. The precipitation algorithm incorporates orographic predictors including wind fields, valley exposition, and boundary layer height, with a subsequent bias correction. The temperature algorithm is based on statistical downscaling of atmospheric temperatures. Here we present the CHELSA (Climatologies at high resolution for the earth’s land surface areas) data of downscaled model output temperature and precipitation estimates of the ERA-Interim climatic reanalysis to a high resolution of 30 arc sec. Moreover, latitudinal similarities in the distribution offer potential in identifying candidate locations for reciprocal transfer of knowledge on environmental design and operation.High-resolution information on climatic conditions is essential to many applications in environmental and ecological sciences. Variations in temperature exist, even between closely spaced sites – emphasizing the need to use location-specific data for enhancing the accuracy of the weather-related analysis. Observed trends agree with conventional wisdom however, sparse coverage in populated areas such as Africa and Asia diminish the versatility of the database. Identified spatial variability illustrate the effects of latitude, elevation, landuse and nearest coastline. This research explored geospatial distributions of key HOF (2009) climatic parameters: temperature (dry-bulb, wet-bulb, dew-point and mean) and degree-days at various temporal scales. ASHRAE-HOF is the most widely used climatic design conditions database for building energy and HVAC professionals but gap exists in literature on the geospatial and temporal distributions of the HOF dataset. With so many of the renewable energy sources being significantly dependent on weather, it is essential that the temporal and geospatial variability and distribution of climatic design parameters are investigated for effective planning and operation. Understanding the climate and location aspects are usually the first step in energy applications – from buildings to renewable energy.

Climatic data for region x