Step 1: Start with the Input folder - Follow the steps inside the folder E-OBS-Data/E-OBS_DATA.txt
Step 2: Computation of climate indices - Use Code/Climate Indices/ (all the Rscript). To use DroughtIndices_1.R and DroughtIndices_2.R first compute potential evapotranspiration using PET.R
Step 3: Output produced would be similar to output of Output/ClimateIndices/Climateindices.mat
Step 4: Produce the figure 2 shown in manuscript to compare the output
Step 5: Correlation matrix computation - Code/Correlation/corrplot.m (load the climate indices computed that is Climateindice.mat). Use the code to produce the output similar to Output/Correlation/correlationvalues.mat.
* Abbreviation for variables in correlationvalues.mat - rhioj & pvai1j (i is for Pearson, Kendall and Spearman (1,2, and 3 respectively) & j is for months). Further rho is for correlation and pval is for significance. Results produced will be similar to figure 3 of the manuscript.
Step 6: To do the trend analysis run the Code/Mann-Kendall & Sens Slope/Mann_Kendall_Sens_Slope.R. 
Step 7: Output produced from the code is saved as a text file in Output\Mann-Kendall & Sens Slope\Mann_Kendall_Sens_Slope.txt. For each 72 indices and each month it is stored seperately. There are 3 columns arrranged in order of mann-kendall (Z value) to determine increasing or decreasing, Sen's slope value (% change per month), Significance level. There are 258735 values which are equivalent to 705x367 (latxlon) for European continent. Results produced will be similar to figure 5 and 6 of the manuscript.
Step 8: To produce figure 4 and 7, do the statistical analysis on the obtained value from the Output\Mann-Kendall & Sens Slope\Mann_Kendall_Sens_Slope.txt.