The following files are the result of the following article: High resolution bioclimatic surfaces for southern Peru: an approximation to the climatic reality for biological conservation. Here are 12 surfaces for maximum temperature, 12 for minimum temperature, 12 for average temperature, 12 for precipitation and 19 bioclimatic surfaces for the study area. Details of some of the published figures are also included. Where: Bios asc = 19 bioclimatic surfaces in .asc format. Bios tif = 19 bioclimatic surfaces in .tif format. Prec = 12 monthly precipitation surface in .tif format. Tmax = 12 monthly maximum temperature surfaces in .tif format. Tmin = 12 monthly minimum temperature surfaces in .tif format. The names of the bioclimatic surfaces correspond to the following: bio1=average annual mean temperature (°C), bio2=average diurnal range (°C), bio3=isothermality, bio4=temperature seasonality (standard deviation), bio5=maximum temperature of the warmest month (°C), bio6=minimum temperature of the coldest month (°C), bio7=annual temperature range (°C), bio8=average temperature of the wettest quarter (°C), bio9=average temperature of the driest quarter (°C), bio10=average temperature of the warmest quarter (°C), bio11=average temperature of the coldest quarter (°C), bio12=annual precipitation (mm/year), bio13=precipitation of the rainiest month (mm/month), bio14=precipitation of the driest month (mm/month), bio15=precipitation seasonality (coefficient of variation), bio16=precipitation of the wettest quarter (mm/quarter), bio17=precipitation of the driest quarter (mm/quarter), bio18=precipitation of the warmest quarter (mm/quarter), bio19=precipitation of the coldest quarter (mm/quarter). The names of each climatic surface correspond to the following: jan = January, feb = February, mar = March, apr = April, may = May, jun = June, jul = July, aug = August, sep = September, oct = October, nov = November, dic = December.

Climatic and bioclimatic surfaces were elaborated for southern Peru (Arequipa, Moquegua and Tacna). For the interpolations, meteorological information from in-situ stations, as well as orographic and geographic covariates were used. Statistical evaluations gave good results, showing some differences with other models also performed for the area. These data will contribute to a better understanding of the ecoclimatic requirements of the species in terms of ENMs and SDMs.