This folder contains a dataset of bias-adjusted climate change simulations for Europe based on the EURO-CORDEX Regional Climate Models simulations. The data contains daily variables (precipitation, mean, minimum and maximum temperature) for the period 1981-2100; the period 1981-2010 is referred to as historical, whereas projections for the period 2011-2100 are driven by differnt emission sceanrios (RCP2.6, RCP4.5, RCP8.5). The full set of data inludes around 100 model simulations, but the GCM/RCM/scenario matrix is vastly heterogeneous. The full list of simulations is provided in the 'LIST_of_MODELS.pdf' file, also in this folder. The original EURO-CORDEX runs have been bias-adjusted by means of a technique developed by Lange (2019), namely a trend preserving, parametric quantile mapping, designed to robustly adjust biases in all percentiles. The EOBS dataset over 1981-2010 is used as reference for the bias-adjustment. Compared to the original EURO-CORDEX simulations, two main differences are worth noting: 1) the spatial domain is not as extended as the origibal EURO-COREX one (Iceland, for insatnce, is not included). In addition, data are provided only over land, where EOBS data is available. The domain is defined accorindg to the following grid: gridtype = lonlat gridsize = 180500 xsize = 475 ysize = 380 xname = lon xlongname = "longitude" xunits = "degrees_east" yname = lat ylongname = "latitude" yunits = "degrees_north" xfirst = -11.3 xinc = 0.11 yfirst = 30.5 yinc = 0.11 scanningMode = 64 2) the calendar of the bias-adjusted simulations has been homogenied so that all runs use the same proleptic_gregorian calenadr (contrary to the original EURO-CODRDEX runs where the calendar can vary according to the model). The data is organized in folders following this structure: RCM////day//BA where is the diownscaling regional model name, the driving global model, is either the historical or rcp run (rcp26, rcp45, rcp85), and is the meteorological paremeter (pr, tas, tasmin, tasmax). For instance: RCM/CLMcom-CCLM4-8-17/ICHEC-EC-EARTH/historical/day/pr/BA The files are named according to the following structure: _EUR-11-small______day_-_BA-EOBSv23-1981-2010.nc where is the ensemble member of the GCM, is the version number of the RCM, (YYYYMMDD) and (YYYYMMDD) the starting and ending date of the time series in the file. For instance pr_EUR-11-small_ICHEC-EC-EARTH_rcp85_r12i1p1_CLMcom-CCLM4-8-17_v1_day_20110101-20401231_BA-EOBSv23-1981-2010.nc ===================================================================================================================== Before using the data users are kindly asked to read the following papers describing the bias-adjusted technique, the impact of the bias-adjustment on the climate change signal (over Africa) and the application of the dataset to compound hot and dry events over Euorpe: Lange, S. (2019). Trend-preserving bias adjustment and statistical downscaling with ISIMIP3BASD (V1. 0). Geoscientific Model Development, 12(7), 3055–3070. 10.5194/gmd-12-3055-2019 Dosio, A., Lennard, C., & Spinoni, J. (2022). Projections of indices of daily temperature and precipitation based on bias-adjusted CORDEX-Africa regional climate model simulations. Climatic Change, 170(1–2), 13. https://doi.org/10.1007/s10584-022-03307-0 Dosio, A., Spinoni, J., & Migliavacca, M. (2023). Record-breaking and unprecedented compound hot and dry summers in Europe under different emission scenarios. Environmental Research: Climate. https://doi.org/10.1088/2752-5295/acfa1b ===================================================================================================================== Any publication whose results are based on these bias-adjusted data should include a text similar to the following one: The dataset is based on the EURO-CORDEX Regional Climate Model (RCM) simulations that have been bias-adjusted (Dosio et al 2022; Dosio et al 2023) by means of a tecnhique originally developed by Lange (2019). In addition, I would be grateful to be granted co-authorship of peer-reviewed publications making used of the data. ==================================================================================================================== For any enquiry about the dataset please contact alessandro.dosio@ec.europa.eu