The summer standardized precipitation evapotranspiration index (SPEI) dataset for six European regions over the past millennium reconstructed by tree-ring chronologies

A reliable reconstructed dataset is essential for studying the European regional hydroclimatic variations over the past millennium. Here we reconstructed a summer standardized precipitation evapotranspiration index (SPEI) dataset for six European regions over the past millennium based on the tree-ring chronologies and using methods of hierarchical clustering analysis, stepwise regression, and partial least squares regression.The reconstructed regions included most European countries (32.16°N–71.68°N, 10.58°W–40.93°E), which was divided into eight regions based on hydroclimatic variations. We reconstructed the summer hydroclimate (SPEI) series in six regions with an annual resolution, including I. British Isles region (BRI), II. Northern Continental region (NCO), III. Northwestern Coastal region (NWC), IV. Western Mediterranean region (WME), V. Eastern Mediterranean region (EME), VI. Southern Black Sea region (SBS). Among the six regional reconstructed series, the longest was in the NCO with 1490 years (517–2006 CE). In the BRI, the reconstructed period was 1649–2003 CE. In the NWC, reconstructions spanned 1623–2010 CE. In the WME, the reconstructed period was 1515–2008 CE. In the EME, the reconstructed period was 1647–2008 CE. In the SBS, the reconstruction spanned 1455–2004 CE.The file of our dataset was named “The hydroclimatic variations series in six European regions over the past millennium.xlsx”. It has six worksheets according to the reconstructed regions; each worksheet contains ten columns, including the reconstructed year (year), reconstructed SPEI value (value), ±95% confidence interval for the uncertainty range (-95% and 95%), mean value over the reconstructed period (mean), R2pr (predicted R2), RMSE during the calibration and validation periods (RMSEC and RMSEV), the number of total chronologies samples available during each period (NTS), and the number of chronologies samples selected for the calibration equation in each period (NCS). This dataset can be read directly using standard data processing and analysis software such as Excel, MATLAB, and Python. Notably, the 95% confidence interval for the uncertainty range of our reconstruction differs in various regions and during various periods. Therefore, we recommend that users choose the functional area and period according to their scientific needs.