diff --git a/07_Modelling/01_Input/BC_Generation/Code/NARCLIM_Download_and_Processing/Preparing_Raw_NARCLIM_for_RMA_preprocessing.py b/07_Modelling/01_Input/BC_Generation/Code/NARCLIM_Download_and_Processing/Preparing_Raw_NARCLIM_for_RMA_preprocessing.py
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+++ b/07_Modelling/01_Input/BC_Generation/Code/NARCLIM_Download_and_Processing/Preparing_Raw_NARCLIM_for_RMA_preprocessing.py
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+# -*- coding: utf-8 -*-
+"""
+Created on Fri Jun 08 14:34:51 2018
+
+@author: z5025317
+"""
+#####################################----------------------------------
+#Last Updated - March 2018
+#@author: z5025317 Valentin Heimhuber
+#code for generating P and ET time series as input to the HUNTER AWBM and RMA models
+#Inputs: Uses CSV files that contain all 12 NARCLIM model runs time series for 1 grid cell created with: P1_NARCliM_NC_to_CSV_CCRC_SS.py
+#####################################----------------------------------
+
+#####################################----------------------------------
+#Load packages
+#####################################----------------------------------
+import numpy as np
+import os
+import pandas as pd
+import glob
+import matplotlib
+import matplotlib.pyplot as plt
+from datetime import datetime
+from datetime import timedelta
+from matplotlib.backends.backend_pdf import PdfPages
+from ggplot import *
+matplotlib.style.use('ggplot')
+#
+# Set working direcotry (where postprocessed NARClIM data is located)
+os.chdir('C:/Users/z5025317/OneDrive - UNSW/Hunter_CC_Modeling/07_Modelling/01_Input/BC_Generation/ClimateData/')
+#set directory path for output files
+output_directory = 'NARCLIM_Processed/Rainfall/'
+#output_directory = 'J:/Project wrl2016032/NARCLIM_Raw_Data/Extracted'
+if not os.path.exists(output_directory):
+    os.makedirs(output_directory)
+    print('-------------------------------------------')
+    print("output directory folder didn't exist and was generated")
+    print('-------------------------------------------')
+    print('-------------------')
+            
+#####################################----------------------------------
+#set input parameters
+Model_site = 'HUNTER' # 'Belongil'
+Clim_var_type  = "pracc"   #  '*' will create pdf for all variables in folder  "pracc*|tasmax*"        
+Bias_Corrected  = True
+#####################################---------------------------------- 
+ 
+Scenarios = ["NNRP_R1_1950",	"NNRP_R2_1950",	"NNRP_R3_1950",	'CCCMA3.1_R1_2020',	'CCCMA3.1_R2_2020',	'CCCMA3.1_R3_2020',	
+             'CSIRO-MK3.0_R1_2020',	'CSIRO-MK3.0_R2_2020',	'CSIRO-MK3.0_R3_2020',	'ECHAM5_R1_2020','ECHAM5_R2_2020',
+             'ECHAM5_R3_2020','MIROC3.2_R1_2020',	'MIROC3.2_R2_2020',	'MIROC3.2_R3_2020']
+
+#write the key plots to a single pdf document
+pdf_out_file_name = Clim_var_type + '_summary_time_series_plots_NARCliM_summary_.pdf'
+pdf_out_path = output_directory +'/' + pdf_out_file_name
+#open pdf and add the plots
+with PdfPages(pdf_out_path) as pdf:
+    for scenario in Scenarios:
+        #scenario = 'CCCMA3.1_R1_2020'
+        print scenario[:-5]
+        Catchment_directories = glob.glob('./NARCLIM/*')
+        Output_df = pd.DataFrame()
+        for Catchment_dir in Catchment_directories:
+            Filename = os.path.basename(os.path.normpath(Catchment_dir))
+            Catchment = 'Catchment' + '_' + Filename.split('_', 2)[1]
+            if Bias_Corrected:
+                Catchment_dir = Catchment_dir + '/Bias_corrected'   
+            if scenario[:4] == 'NNRP': 
+                clim_var_csv_path = glob.glob(Catchment_dir + '/' + Clim_var_type + '_NNRP*')
+                Full_df = pd.read_csv(clim_var_csv_path[0], index_col=0, parse_dates = True)
+                Full_df.index = Full_df.index.to_period('D')
+                IVM_df = Full_df.filter(regex= scenario[:-5])
+                IVM_df = IVM_df.iloc[:,:].loc[(IVM_df.index < '2010-01-01')]
+                #calculate daily ETo from the NARCLIM ET flux
+                if Clim_var_type == 'evspsblmean' or Clim_var_type == 'potevpmean':
+                    IVM_df = IVM_df.multiply(86400) 
+                IVM_df.columns = [Catchment]
+                Output_df = pd.concat([Output_df, IVM_df], axis=1)
+            else:
+                clim_var_csv_path = glob.glob(Catchment_dir + '/' + Clim_var_type + '_GCMS*')
+                Full_df = pd.read_csv(clim_var_csv_path[0], index_col=0, parse_dates = True)
+                Full_df.index = Full_df.index.to_period('D')
+                IVM_df = Full_df.filter(regex= scenario[:-5])
+                IVM_df = IVM_df.iloc[:,:].loc[(IVM_df.index > '1989-12-31')]
+                if Clim_var_type == 'evspsblmean' or Clim_var_type == 'potevpmean':
+                    IVM_df = IVM_df.multiply(86400) 
+                IVM_df.columns = [scenario[:-5],scenario[:-5], scenario[:-5]]
+                dfa1 = pd.DataFrame(IVM_df.iloc[:,0]).dropna(axis=0)
+                dfa2 = pd.DataFrame(IVM_df.iloc[:,1]).dropna(axis=0)
+                dfa3 = pd.DataFrame(IVM_df.iloc[:,2]).dropna(axis=0)
+                IVM_df = pd.concat([dfa1, dfa2], axis=0)
+                IVM_df = pd.concat([IVM_df, dfa3], axis=0)
+                IVM_df.columns = [Catchment]
+                Output_df = pd.concat([Output_df, IVM_df], axis=1)
+    
+        Output_df.index.name = 'datetime'
+        Output_df_annual = Output_df.resample('A').sum()
+        Output_df_annual = Output_df_annual.replace(0, np.nan)
+        # time series plot annual ALL models
+        plt.title(Clim_var_type + ' -  Time series - for all catchments')
+        Output_df_annual.plot(legend=False)
+        pdf.savefig(bbox_inches='tight', pad_inches=0.4)
+        plt.close()
+
+        #export the pandas data frame as a CSV file within the output directory
+        out_file_name = scenario[:-5] + '_' +  Clim_var_type + '_NARCliM.csv'
+        out_path = output_directory +'/' + out_file_name
+        Output_df.to_csv(out_path)
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