diff --git a/PreProc/inputdata/atm/cam/ggas/create_TIPMIP_emission_file.py b/PreProc/inputdata/atm/cam/ggas/create_TIPMIP_emission_file.py
new file mode 100644
index 0000000..d34371d
--- /dev/null
+++ b/PreProc/inputdata/atm/cam/ggas/create_TIPMIP_emission_file.py
@@ -0,0 +1,90 @@
+import numpy as np
+import sys
+import xarray as xr
+from scipy.io import netcdf
+import pandas as pd
+
+
+# load grid area from fx
+dataset = xr.open_dataset('/mnt/bgcdata-ns2980k/ffr043/TipESM/data/NorESM2-LM/1pctCO2/fx/areacella_fx_NorESM2-LM_1pctCO2_r1i1p1f1_gn.nc')
+areavar = dataset['areacella']
+totarea = areavar.sum().values
+
+# load example emission file
+inpath = '/mnt/bgcdata-ns2980k/ffr043/TipESM/data'
+dataset = xr.open_mfdataset(inpath+'/emissions-cmip6_CO2_anthro_surface_175001-201512_fv_1.9x2.5_c20181011.nc', decode_times=False, format="NETCDF3_64BIT")
+co2var = dataset['CO2_flux']
+
+# distribute emissions over time and space
+# translated to python from matlab based on script by J. Schwinger
+nyears = 250
+nmonth = nyears * 12
+nlon = 144
+nlat = 96
+start_year = 1
+time_bnds = np.zeros((2, nmonth))
+time = np.zeros(nmonth)
+date = np.zeros(nmonth, dtype=np.int32)
+co2flx = np.zeros((nmonth, nlat, nlon))
+dayim = [31, 28, 31, 30, 31, 30, 31, 31, 30, 31, 30, 31]
+days = np.cumsum([0] + dayim[:-1]) + 1
+daye = np.cumsum(dayim)
+
+# calculate emissions based on TCRE (Arora et al., 2020)
+TCRE = 1.32 #°C EgC-1
+E = 1000 * 0.02 / TCRE #GtC yr-1
+Edata =  np.ones((nmonth, 1)) * E 
+
+# (assumed) midpoint of each month in the format MMDD
+date_mint = [116, 215, 316, 416, 516, 616, 716, 816, 916, 1016, 1116, 1216]
+
+# Set emissions constant over each month or not
+constant_em = True
+
+for iy in range(1, nyears + 1):
+    for im in range(1, 13):
+        idx = (iy - 1) * 12 + im - 1
+        time_bnds[0, idx] = 365 * (iy - 1) + days[im - 1] - 1
+        time_bnds[1, idx] = 365 * (iy - 1) + daye[im - 1]
+        time[idx] = np.sum(time_bnds[:, idx]) / 2.0
+        date[idx] = (1850 + (iy - 1)) * 10000 + date_mint[im - 1]
+        
+        # Spatially, emissions are distributed evenly over the sphere and months
+        if start_year <= iy < start_year + nyears:
+            if constant_em == True:
+                # unit correction - Gt C yr-1 to kg CO2 s-1
+                co2flx[idx, :, :] = Edata[idx] * 1e12 * 3.664 / totarea / 86400.0 / 365.0
+	    else:
+                # unit correction sec month-1
+                dt = (time_bnds[1, idx] - time_bnds[0, idx]) * 86400.0
+                # unit correction - Gt C month-1 to kg CO2 s-1
+                co2flx[idx, :, :] = Edata[idx] * 1e12 * 3.664 / dt / totarea / 12
+        else:
+            co2flx[idx, :, :] = 0.0
+
+# write emissions to example dataset
+dataset = dataset.isel(time=slice(0, nmonth))
+dataset['CO2_flux'].values = co2flx
+
+# assign attributes
+dataset = dataset.assign_attrs({'data_title':'Annual Anthropogenic Emissions of CO2 based on TCRE prepared for TIPMIP'})
+dataset = dataset.assign_attrs({'data_creator':'F. Froeb (friederike.frob@uib.no)'})
+dataset = dataset.assign_attrs({'creation_date':'2024-08-03'})
+
+#set encoding for netcdf file
+encoding = {
+'time':{'_FillValue': None},
+'time_bnds':{'_FillValue': None},
+'lon':{'zlib': True, 'shuffle': False, 'complevel': 1, 'fletcher32': False, 'contiguous': False, 
+     'dtype': 'float64', '_FillValue':None},
+'lat':{'zlib': True, 'shuffle': False, 'complevel': 1, 'fletcher32': False, 'contiguous': False, 
+     'dtype': 'float64', '_FillValue':None},
+'CO2_flux':{'zlib': True, 'shuffle': True, 'complevel': 9, 'fletcher32': False, 'contiguous': False,
+     'dtype': 'float32', 'missing_value': 1e+20, '_FillValue': 1e+20}
+}
+
+#write netcdf
+dataset.to_netcdf('/mnt/bgcdata-ns2980k/ffr043/TipESM/data/emissions-ESM-tipmip_CO2_anthro_surface_185001-209912_fv_1.9x2.5_c20240823_cE.nc', mode="w", format="NETCDF3_64BIT", encoding=encoding, unlimited_dims='time')
+
+
+
diff --git a/README.md b/README.md
index 57b5969..70cf157 100644
--- a/README.md
+++ b/README.md
@@ -5,6 +5,7 @@ Miscellaneous pre- and post-processing scripts for NorESM
     - mg_budget_plot.py: Script for plotting the the tendency terms that contribute to the total tendencies of the cloud microphysics scheme by Morrison and Gettelman in NorESM2/CAM6
 - PostProc: Scripts for post processing model output
     - rcat_noresm.sh: Concatenate and compress NorESM model output
+- PreProc: Scripts for preparing model input datasets
 - Utilities: Miscellaneous scripts and programs
     - buildCPRNC_NIRD.sh: File to build the cprnc tool needed by rcat_noresm.sh
     - test_rcat_noresm.sh: Unit tests for rcat_noresm.sh