- Latest period
- 2100-12-01
- Value · mm
- 329.37
- Valid periods
- 1032 / 1032
- Latest area coverage
- 100.0%
- Spatial scale
- country
- Release status
- Provisional / unconfirmed
Absolute values; no anomaly baseline applied. Reviewed 2026-09-15.
Method, coverage and provenance
Area-weighted regional monthly means from 26 CMIP6 models under SSP2-4.5. Value is the across-model median; the p10 and p90 fields carry the LOWEST and HIGHEST member values, not interpolated percentiles, because five named models are not a sample a percentile can be read from. Temperature converted from K; precipitation converted from kg m-2 s-1 to mm per month using each model's OWN calendar month length. Native calendars: 360_day: 2 (KACE-1-0-G, UKESM1-0-LL); 365_day: 9 (BCC-CSM2-MR, CAMS-CSM1-0, CESM2, CMCC-CM2-SR5, CanESM5-CanOE, FGOALS-f3-L, FGOALS-g3, INM-CM4-8, INM-CM5-0); gregorian: 6 (CNRM-CM6-1, CNRM-CM6-1-HR, CNRM-ESM2-1, IPSL-CM6A-LR, MIROC-ES2L, MIROC6); julian: 1 (IITM-ESM); noleap: 4 (GFDL-ESM4, MCM-UA-1-0, NorESM2-MM, TaiESM1); proleptic_gregorian: 4 (ACCESS-CM2, AWI-CM-1-1-MR, MPI-ESM1-2-LR, MRI-ESM2-0). Output periods are real calendar months. Each model is weighted on its own grid; the provenance grid figures describe the COARSEST member, which bounds the ensemble, rather than any one model's grid.
26 CMIP6 models, one run each. One run per model is deliberate: a model contributing fifty members would otherwise outvote the rest of the archive. The value is the across-model mean and the p10/p90 fields are the 10th and 90th percentiles of the member distribution, the range the IPCC AR6 Atlas reports for regional projections. Members are weighted by climwip-independence/1: the ClimWIP independence term (Knutti et al. 2017; Brunner et al. 2020), which shares one vote between models whose historical runs resemble each other, with the shape parameter set to this region's median inter-model distance rather than calibrated by a perfect-model test; there is no performance term, so no model is up- or down-weighted for matching observations. The weighting leaves an effective 25.1 of 26 models (Kish), which is the number the spread is actually estimated from. This is not a calibrated confidence interval, not a probability distribution over outcomes, and carries no downscaling or bias correction — regional absolute values retain each model's own bias. Ensemble membership is part of the campaign digest, because adding or removing a model changes both the mean and the spread. A month is emitted only where every member supplies it. Realisation note: for CESM2 (r1i1p1f1 historical, r4i1p1f1 SSP2-4.5); MCM-UA-1-0 (r1i1p1f1 historical, r1i1p1f2 SSP2-4.5), the historical run is a different realisation from the projection published here. CDS serves whichever realisation it holds for a named model, and the request carries no variant key. The independence weighting takes its distances from the historical fields, so for these members the run that sets the weight is not the run being weighted. The last 12 month(s) are NOT model output: no member of the ensemble runs past 2099-12-01, so that month's value is repeated to the end of the series, and each such point carries carried_from. A repeated value understates a rising trend, which under a high scenario is steepest at the end, so the closing figure is a floor rather than a projection.
Geometry: admin0:sha256:afff7d5dc22279a90216f1ad1c8d50dff130f6b176e5720ce968f306a03c7749 · weighting: area · method: coterran-cmip6-ensemble/1.
2 intersecting cells; 1.1 effective cells · 2.79° × 2.81° grid. Grid-limited: the regional result draws on fewer than four effective cells.
Minimum valid area: 95.0% · coverage is represented area, not accuracy.
Campaign SHA-256: 04849d821b9da32b5ebb052a46751df63480a7168561b832e72009a1a28ae3f9
28 input checksums and software versions
geometry · admin0.json.gz · 492,727 bytesafff7d5dc22279a90216f1ad1c8d50dff130f6b176e5720ce968f306a03c7749
campaign · campaign.json · 436,412 bytesecd5f66e89c5e750159802107a974d404d2f42444a165ffbd74406f424b65314
climate_grid · pr-ssp2_4_5-access_cm2.zip · 104,298,696 bytes669198a2631a967cfbcd9ce4689569524e0c56359d15f97e6d3bb11808e92a8c
climate_grid · pr-ssp2_4_5-awi_cm_1_1_mr.zip · 282,072,366 bytes7be6c218f50e09eecbc8cd72559a92ed9721fda472171a9cf0e96e8bedf55927
climate_grid · pr-ssp2_4_5-bcc_csm2_mr.zip · 211,656,836 bytes5e6275898852ef36415be3a0c6a73a40f956d64a351a413aa614ac43f3b51f12
climate_grid · pr-ssp2_4_5-cams_csm1_0.zip · 209,194,636 bytes80a08a1f26a7de337cb489f168beb3b2fa4cde09c4d72228aeee458e9552e721
climate_grid · pr-ssp2_4_5-canesm5_canoe.zip · 31,431,906 bytesa29cae762d3884ae1cf24ec8f6b3e6eea427b345620f2f385998c566b4b63f35
climate_grid · pr-ssp2_4_5-cesm2.zip · 179,279,220 bytesb748c0ca95aba2e7cdd58a439a3f26ed8fdbadc77f9c75cd7b6711f615d88855
climate_grid · pr-ssp2_4_5-cmcc_cm2_sr5.zip · 179,121,658 bytes8f1e6408544f92b75c97cc095bd43685c72aea2d6829617ad3663d6c4d8291e8
climate_grid · pr-ssp2_4_5-cnrm_cm6_1.zip · 105,557,617 bytes5490a6181ffaf48bb6fa17295adb7c7bb16dfbefcd294592a76c4b097d785855
climate_grid · pr-ssp2_4_5-cnrm_cm6_1_hr.zip · 771,427,567 bytes5f21b0b5ff4d9bc54f6f824703de85d8817df75370be2dbcbfb3f783de171f67
climate_grid · pr-ssp2_4_5-cnrm_esm2_1.zip · 105,532,106 bytesd61bd25cf3455d5f4560613ef57ad80f72f7c63fde7984ecddc231686611e17a
climate_grid · pr-ssp2_4_5-fgoals_f3_l.zip · 214,299,401 bytese12e967bd73a2c1f4ff1efc804f6a287e1c2f26fac2da4ebf1973a54350b07bd
climate_grid · pr-ssp2_4_5-fgoals_g3.zip · 59,892,539 bytes02a79aa8f3a44e2205f21c3eef9f9ab446fec6d94d5e37d5f4b3fee8346aef21
climate_grid · pr-ssp2_4_5-gfdl_esm4.zip · 167,163,121 bytes2fc9e503746d235ccdec8f7a8902be4be0147667038f039fa39a0058892dcbf6
climate_grid · pr-ssp2_4_5-iitm_esm.zip · 67,285,761 bytes6578d61e38ec46e318eda7d9bb8884f63dd5275d4f20a4ac4d4f3edb1a969c17
climate_grid · pr-ssp2_4_5-inm_cm4_8.zip · 72,003,209 bytes5ec1621c80a7959d1864f16720dda6870fbdf2f5353f3119f78af119f668b691
climate_grid · pr-ssp2_4_5-inm_cm5_0.zip · 72,000,872 bytes177e3d5b5c65f76b4d01cc8003048d5cb24511b0d0ab42c9a073d5885ccde35f
climate_grid · pr-ssp2_4_5-ipsl_cm6a_lr.zip · 68,185,400 bytese2f4f50536541239408d014e9cb50509332ec34b4e6b061e36d97e35320c168c
climate_grid · pr-ssp2_4_5-kace_1_0_g.zip · 91,913,699 bytesa100ea4b0ec735b467044f9cba6a22dc3e9139a31bb76342dcce8aad622b5b79
climate_grid · pr-ssp2_4_5-mcm_ua_1_0.zip · 32,019,550 bytes80b501b9db21f162b7f7f7d77f5f54ef3ecf571f1dbf49ca6bf864b9085e9210
climate_grid · pr-ssp2_4_5-miroc6.zip · 105,682,209 bytesc822897b70931fd388eed8b716eeccf88a90c60f4714775db3b3e297a114c074
climate_grid · pr-ssp2_4_5-miroc_es2l.zip · 27,232,162 bytescc8c5ddf0ff26756e8e97fcef27efefc02e5b16204b7d9b8e91b90a6df618c13
climate_grid · pr-ssp2_4_5-mpi_esm1_2_lr.zip · 57,308,451 bytes277634391505f0fc8c8e1384399a5e70f0a1cc74f61b1d3db2cb36084df48e06
climate_grid · pr-ssp2_4_5-mri_esm2_0.zip · 167,449,565 bytes1ad63cd5a1f0b40bea754a2c5f9c59c74421c3e407c45c55a4c9188584be6c73
climate_grid · pr-ssp2_4_5-noresm2_mm.zip · 177,902,039 bytes2b2666363fbc691f1e41671c0a90320445269b4c2a2937613fe5d969a25dc7b6
climate_grid · pr-ssp2_4_5-taiesm1.zip · 177,317,442 bytes1436c564a9a94b62daad7a3e401a1c818303c40cb57e020106a0564852653ee7
climate_grid · pr-ssp2_4_5-ukesm1_0_ll.zip · 100,063,527 bytesbf7f050f3d67ca3a7d6f8b9435ac852bcf753651ba6527ef64b278de256f09ef
numpy==2.5.3 · pandas==3.0.5 · xarray==2026.7.0 · shapely==2.1.2 · netCDF4==1.7.4 · Python==3.14.6
Regional input SHA-256: e4e9f5ae293009c9292b4b790aebc14df064cc4bf46aebe36ea3aef25e33c9ea
Source version: projections-cmip6 · revision: campaign-04849d821b9da32b5ebb052a46.
Provider issue time unavailable. Retrieved 2026-09-14T23:30:31Z.