Dear Mulugeta Fiseha,
You can easily download CMIP6 data for a specific area with following link.
https://cds.climate.copernicus.eu/cdsapp#!/dataset/projections-cmip6?tab=form
If your selected area is composed of some "Netcdf" pixels (I mean your area is larger than "Netcdf" spatial resolution) you can select your desirable point (the pixel in which your point is) using ARCGIS. > Arc toolbox > Multidimension tools > Make netcdf table view > dimension values.
Hi Mulugeta Fiseha here is a Python script to download data from CMIP6 for a specific region using Python that can be useful for you:
"""
! pip install esgf-pyclient==0.3.0
!pip install requests-cache==0.4.1
from pyesgf.search import SearchConnection
import os
import requests_cache
requests_cache.core.CachedSession
#from pyesgf.search import SearchConnection
#import os
#import pandas as pd
import requests
from tqdm import tqdm
import xarray as xr
#import requests_cache
requests_cache.core.CachedSession
lista_modelos= ['ACCESS-CM2','ACCESS-ESM1-5','AWI-CM-1-1-MR','BCC-CSM2-MR',
'CAMS-CSM1-0','CAS-ESM2-0','CMCC-CM2-SR5','CMCC-ESM2','CanESM5',
'EC-Earth3','EC-Earth3-Veg','EC-Earth3-Veg-LR','FGOALS-f3-L',
'GFDL-ESM4','IITM-ESM','INM-CM4-8','INM-CM5-0','IPSL-CM6A-LR',
'KACE-1-0-G','MIROC6','MPI-ESM1-2-HR','MPI-ESM1-2-LR','MRI-ESM2-0',
'NESM3']
conn = SearchConnection('https://esgf-node.llnl.gov/esg-search', distrib=True)
ctx = conn.new_context(project='CMIP6',
experiment_id=['historical'],
realm=['atmos'],
variable='uas',
frequency='mon',
source_type=['AOGCM'],
variant_label='r1i1p1f1',
source_id= lista_modelos
)
ctx.hit_count
ctx.facet_counts['source_id'].keys()
# Obtain urls lists
lista_urls=[]
for i in range(len(ctx.search())):
try:
result = ctx.search()[i]
print(result.dataset_id,'..............ok')
lista_urls.append(result)
except:
pass
print('-------------------------------')
print('Lista finales')
lista_urls
# Download the first model for example
tr=[x for x in lista_urls if lista_modelos[0] in x.dataset_id]
files = tr[0].file_context().search()
lista=[]
for i in range(len(files)):
lista.append(files[i].opendap_url)
print(lista)
ds = xr.open_mfdataset([x for x in lista], chunks={'time': 120}, combine='nested', concat_dim='time')
ds_mei=ds.where((ds.lon>=-90+360) & (ds.lon=6) & (ds.lat
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