# API Request failing with "Your request is too large" **URL:** \ **Category:** API\ **Tags:** api\ **Created:** [20 January 2026 09:51 UTC](https://forum.ecmwf.int/t/api-request-failing-with-your-request-is-too-large/14622 "2026-01-20T09:51:11Z")\ **Posts on this page:** 3\ **Page:** 1
**Author:** ![Petar\_Dimitrov](https://forum.ecmwf.int/letter_avatar_proxy/v4/letter/p/3d9bf3/32.png) [@Petar\_Dimitrov](https://forum.ecmwf.int/u/Petar_Dimitrov)\ **Post date:** [20 January 2026 09:51 UTC](https://forum.ecmwf.int/t/api-request-failing-with-your-request-is-too-large/14622/1 "2026-01-20T09:51:11Z")
Hi, I am trying to download ERA5-Land data using the cdsapi but get this error: 03 Client Error: Forbidden for url: [https://cds.climate.copernicus.eu/api/retrieve/v1/processes/reanalysis-era5-land/execution](https://cds.climate.copernicus.eu/api/retrieve/v1/processes/reanalysis-era5-land/execution) cost limits exceeded Your request is too large, please reduce your selection. My request includes 5 variables, 1 year, 12 months, 31 days, 12 hours and a single grid point (I am using a very small bounding box to ensure only one grid point is retrieved; ‘area’: [42.29, 24.7, 42.2, 24.79]). The format is grib, unarchived. Is it normal for the API to fail downloading such a small dataset (a table with 8760 rows and 6 columns if we count “valid\_time”)? I think the limit is 10 GB. Am I doing something wrong? Here is my code: client = cdsapi.Client() dataset = ‘reanalysis-era5-land’ request = { ‘variable’: [“2m\_dewpoint\_temperature”, “2m\_temperature”, “surface\_solar\_radiation\_downwards”, “10m\_u\_component\_of\_wind”, “10m\_v\_component\_of\_wind”], ‘year’: [‘2025’], ‘month’: [‘01’, ‘02’, ‘03’, ‘04’, ‘05’, ‘06’, ‘07’, ‘08’, ‘09’, ‘10’, ‘11’, ‘12’], ‘day’: [‘01’, ‘02’, ‘03’, ‘04’, ‘05’, ‘06’, ‘07’, ‘08’, ‘09’, ‘10’, ‘11’, ‘12’, ‘13’, ‘14’, ‘15’, ‘16’, ‘17’, ‘18’, ‘19’, ‘20’, ‘21’, ‘22’, ‘23’, ‘24’, ‘25’, ‘26’, ‘27’, ‘28’, ‘29’, ‘30’, ‘31’], ‘time’: [‘00:00’, ‘01:00’, ‘02:00’, ‘03:00’, ‘04:00’, ‘05:00’, ‘06:00’, ‘07:00’, ‘08:00’, ‘09:00’, ‘10:00’, ‘11:00’, ‘12:00’, ‘13:00’, ‘14:00’, ‘15:00’, ‘16:00’, ‘17:00’, ‘18:00’, ‘19:00’, ‘20:00’, ‘21:00’, ‘22:00’, ‘23:00’], ‘data\_format’: ‘grib’, “download\_format”: “unarchived”, ‘area’: [42.29, 24.7, 42.2, 24.79] } target = ‘download2025.grib’ client.retrieve(dataset, request, target) ---
**Author:** ![Mark\_Elliott1](https://forum.ecmwf.int/letter_avatar_proxy/v4/letter/m/c68b51/32.png) [@Mark\_Elliott1](https://forum.ecmwf.int/u/Mark_Elliott1)\ **Post date:** [20 January 2026 16:29 UTC](https://forum.ecmwf.int/t/api-request-failing-with-your-request-is-too-large/14622/2 "2026-01-20T16:29:39Z")
See the download web page [ERA5-Land hourly data from 1950 to present](https://cds.climate.copernicus.eu/datasets/reanalysis-era5-land?tab=download) Only one year/month is selectable. For ERA5 “Select all” is an option, but for some reason not for ERA5-Land I doubt that helps much 🙂 ---
**Author:** ![Petar\_Dimitrov](https://forum.ecmwf.int/letter_avatar_proxy/v4/letter/p/3d9bf3/32.png) [@Petar\_Dimitrov](https://forum.ecmwf.int/u/Petar_Dimitrov)\ **Post date:** [21 January 2026 07:30 UTC](https://forum.ecmwf.int/t/api-request-failing-with-your-request-is-too-large/14622/3 "2026-01-21T07:30:00Z")
Dear Mark\_Elliott1, thank you for your response. For other who may be interested in this topic I found this post in the forum explaining the limits for data download. I used the code linked in this post to download data month by month using a for loop. I had to combine the files after that, but it is better than nothing. > [@Request too large, item limit for ERA5-Land data](https://forum.ecmwf.int/t/request-too-large-item-limit-for-era5-land-data/1488/5): > > Stricter selection limits on ERA5-Land were unfortunately necessary as the large requests were causing backlogs in the Climate Data Store (CDS) queueing system. Since imposing the limits a much higher level of service across the system was achieved, a 20-50% increase in quantity of data delivered per day.  It became obvious quite quickly that this limit impacted our users more than anticipated and so the Climate Data Store (CDS) team looked into ways of improving the situation while preserving …