CHclim25 | Swiss Eco-Climatic GIS data | Example dataset for MigClim.genClust | Bioclim variables for Southern South America | Dryad data


Climatic dataset for Switzerland downscaled from MeteoSwiss gridded data at 1km to 25m using local regressions. It includes daily mean/max/min temperatures (TabsD/TminD/TmaxD), daily sum of precipitation (RhiresD ) and  daily relative sunshine duration (SrelD)), and daily potential incoming solar radiations (SradD) for 1981 to 2015. Gridded climate change scenarios of temperature and precipitations at 0.02°D (~2.2 km) from the CH2011 initiative (reviewed in Zubler et al. 2014) were downscaled with bilinear interpolation at 25m and applied to calculated climatic layers (A1B, A2 and the mitigation scenario RCP3PD are available for time slices 2020-2049, 2045-2074, and 2070-2099). 

Available upon request at

Technical report: Broennimann, O. (2018). CHclim25: A high spatial and temporal resolution climate dataset for Switzerland. Ecospat laboratory, University of Lausanne, Switzerland.

CHclim25.pdf  (106 Ko)


Swiss Eco-Climatic GIS data

This pdf shows the different eco-climatic GIS layers available for Switzerland. Information is provided about the methods used for the creation of the layers as well as their interdependances and correlations.

CHpreds2.pdf  (24442 Ko)


Example dataset for MigClim.genClust

This dataset is to be used with the MigClim.genClust function implemented in the MigClim R package  (201 Ko)


Bioclim variables for Southern South America

19 bioclimatic variables based on Nix 1985, derived from a dataset of monthly climatic variables (1950-2000, 1 km spatial resolution) created with Anusplin software (Hutchinson 2006). More details on the methods here.

Suggested citation: Pliscoff, P., Luebert, F., Hilger, H. H., & Guisan, A. (2014). Effects of alternative sets of climatic predictors on species distribution models and associated estimates of extinction risk: A test with plants in an arid environment. Ecological Modelling, 288, 166–177

Download zip files: 

Bioclim variables: 1-7 8-14 15-19

Monthly temperature mean: 1-6 7-12

Monthly temperature min: 1-6 7-12

Monthly temperature max: 1-6 7-12

Monthly precipitation: 1-12


Dryad data

  • D'Amen M, Mod HK, Gotelli NJ, Guisan A (2017). Data from: Disentangling biotic interactions, environmental filters, and dispersal limitation as drivers of species co-occurrence. Dryad Digital Repository:
  • Dubuis, A., Pottier, J. & Guisan, A. (2017). Data from:Improving spatial predictions of taxonomic, functional and phylogenetic diversity. Dryad Digital Repository.
  • Pottier J, Malenovský Z, Psomas A, Homolová L, Schaepman ME, Choler P, Thuiller W, Guisan A, Zimmermann NE (2014) Data from: Modelling plant species distribution in alpine grasslands using airborne imaging spectroscopy. Biology Letters  
  • Carvalho SB, Gonçalves J, Guisan A, Honrado J (2015) Data from: Systematic site selection for multispecies monitoring networks. Journal of Applied Ecology  
  • Ndiribe C, Pellissier L, Antonelli S, Dubuis A, Pottier J, Vittoz P, Guisan A, Salamin N (2013) Data from: Phylogenetic plant community structure along elevation is lineage specific. Ecology and Evolution  
  • Bidegaray-Batista L, Sánchez-Garcia A, Santulli G, Maiorano L, Guisan A, Vogler A, Arnedo M (2016) Data from: Imprints of multiple glacial refugia in the Pyrenees revealed by phylogeography and palaeodistribution modelling of an endemic spider. Molecular Ecology  
  • Henry P, Le Lay G, Goudet J, Guisan A, Jahodova S, Besnard G (2009) Data from: Reduced genetic diversity, increased isolation and multiple introductions of invasive giant hogweed in the western Swiss Alps. Molecular Ecology  
  • Pellissier L, Niculita-Hirzel H, Dubuis A, Pagni M, Guex N, Ndiribe C, Salamin N, Xennarios I, Goudet J, Sanders IR, Guisan A (2014) Data from: Soil fungal communities of grasslands are environmentally structured at a regional scale in the Alps. Molecular Ecology  
  • Guisan A, Dubuis A, Vittoz P (2011) Data from: Predicting spatial patterns of plant species richness: a comparison of direct macroecological and species stacking modelling approaches. Diversity and Distributions  


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