the CHELSA algorithm 17 , which provides a more accurate representation of temperature and precipitation in highly complex terrain. Data based on the CHELSA algorithm 17 have already been used to infer e.g. ecological niches 24 , soil nutrients 25,26 , or to assess climate change impacts of forests 27 , insect pests 28 , and biodiversity 29. Mean monthly maximum daily temperatures and mean monthly minimum daily temperatures have been downscaled using the delta change method based on the high-spatial-resolution data taken from CHELSA V.1.2. Monthly precipitation sums have been downscaled by applying the CHELSA algorithm directly on bias corrected GCM data. The CHELSA algorithm allows for representing the effects from changing wind patterns and boundary layer conditions in the process of downscaling, and therefore allows for a better estimation of the km-scale changes in precipitation patterns under future climate projections. Methods Selection of global circulation models (GCMs). Projected future climate variables were taken from four global circulation models (GCMs) driven by two scenarios of representative concentration pathways (RCPs) in a factorial manner. The four selected models originate from the CMIP5 collection of model runs used in IPCC's 5th Assessment Report 30 (IPCC 2013). GCMs are, however, often based on similar code which consequently results in similar output 31,32. We therefore chose models that show a low amount of interdependence to allow for a good representation of uncertainty among available climate projections. Model selection was performed to reduce model interdependence in ensembles (see ref. 32).
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