This page provides an overview of regional forecast skill for the SON 2025 period. Forecast scores are updated automatically every week throughout the competitive period. The current data includes 7 8 forecasts initialized between Thursday 14th August 2025 and Thursday 25th September 2nd October 2025 (inclusive). For a detailed description of the outputs, please refer to the section's overview.
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| title | Forecast window 1 (days 19 to 25) |
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| Team name | Team rank | Model name | Model rank | Global | Tropics | NHem. ExTro. | SHem. ExTro. | NHem. Polar | SHem. Polar | Europe | N. Amer. | S. Amer. | Africa | Asia | Oceania |
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| MicroEnsemble | 1 | MicroDuet | 1 | 0.071078 | 0.108113 | 0.03805 | 0.061059 | 0.035048 | 0.018016 | 0.01022 | 0.052061 | 0.075062 | 0.068081 | 0.074088 | 0.128137 | | MicroEnsemble | 1 | StillLearning | 2 | 0.065073 | 0.098102 | 0.038051 | 0.053051 | 0.032049 | 0.023024 | 0.013019 | 0.048058 | 0.064052 | 0.09102 | 0.067083 | 0.121122 | | MicroEnsemble | 1 | Huracan | 9 | 0.03038 | 0.029034 | 0.026038 | 0.043 | 0.03046 | 0.017018 | 0.002012 | 0.05056 | -0.0013 | -0.044033 | 0.046063 | 0.069089 | | CMAandFDU | 2 | FengshunHybrid | 3 | 0.058063 | 0.103109 | 0.023028 | 0.024021 | 0.017032 | -0.003004 | -0.0 | 0.058062 | 0.091076 | 0.102104 | 0.046061 | 0.052073 | | CMAandFDU | 2 | FengshunAdjust | 4 | 0.058063 | 0.116122 | 0.015017 | 0.017014 | -0.009003 | -0.008005 | 0.009003 | 0.036044 | 0.122118 | 0.104112 | 0.038049 | 0.072078 | | CMAandFDU | 2 | Fengshun | 1110 | 0.001009 | 0.022036 | -0.007004 | -0.016023 | 0.001007 | -0.069076 | 0.012015 | 0.022036 | 0.016019 | -0.006005 | 0.015021 | -0.036026 | | LP | 3 | LPM | 5 | 0.045053 | 0.062071 | 0.034044 | 0.038035 | 0.033049 | -0.002- | 0.006001 | 0.047051 | 0.049037 | 0.029042 | 0.062081 | 0.065087 | | AIFS | 4 | AIFSheraAIFSgaia | 6 | 0.04048 | 0.048074 | 0.035029 | 0.023016 | 0.039041 | -0.002005 | 0.019013 | 0.042047 | 0.068049 | 0.02606 | 0.051056 | 0.042058 | | AIFS | 4 | AIFSgaiaAIFShera | 7 | 0.038045 | 0.063053 | 0.021042 | 0.01027 | 0.02205 | -0.014004 | 0.013021 | 0.037045 | 0.049059 | 0.045 | 0.039064 | 0.026069 | | AIFS | 4 | AIFSthalassa | 8 | 0.03042 | 0.047063 | 0.019025 | 0.018 | 0.027033 | -0.006001 | 0.005006 | 0.026033 | 0.042043 | 0.022043 | 0.039054 | 0.033052 | | scienceAI | 5 | findforecast | 1011 | 0.001004 | 0.001003 | -0.008005 | 0.025 | -0.001005 | 0.01011 | -0.016008 | 0.032036 | -0.006013 | -0.013003 | -0.018016 | 0.03904 | | scienceAI | 5 | zephyr | 12 | -0.0 | -0.0 | -0.0 | -0.0 | -0.0 | -0.0 | -0.0 | -0.0 | -0.0 | -0.0 | -0.0 | -0.0 | | scienceAI | 5 | ngcm | 12 | -0.0 | -0.0 | -0.0 | -0.0 | -0.0 | -0.0 | -0.0 | -0.0 | -0.0 | -0.0 | -0.0 | -0.0 | | KITKangu | 6 | KanguPlusPlus | 12 | -0.0 | -0.0 | -0.0 | -0.0 | -0.0 | -0.0 | -0.0 | -0.0 | -0.0 | -0.0 | -0.0 | -0.0 | | KITKangu | 6 | KanguParametricPrediction | 12 | -0.0 | -0.0 | -0.0 | -0.0 | -0.0 | -0.0 | -0.0 | -0.0 | -0.0 | -0.0 | -0.0 | -0.0 | | KITKangu | 6 | KanguS2SEasyUQ | 3432 | -1.074105 | -1.18522 | -01.987021 | -1.039087 | -0.866907 | -0.931919 | -1.017006 | -01.992017 | -1.24226 | -1.2265 | -01.992058 | -1.242211 | | CliMA | 7 | CliMAWeather2 | 16 | -0.12101 | -0.14117 | -0.153134 | -0.117096 | -0.067052 | -0.01502 | -0.104087 | -0.108093 | -0.148123 | -0.192162 | -0.135115 | -0.098086 | | CliMA | 7 | CliMAWeather | 2119 | -0.31318 | -0.359361 | -0.323343 | -0.387378 | -0.262281 | -0.145158 | -0.286256 | -0.149194 | -0.338321 | -0.396404 | -0.411435 | -0.449433 | | FengWuW2SWindBorne | 8 | FengWu2WeatherMesh | 17 | -0.177176 | -0.197144 | -0.257134 | -0.019316 | -0.134132 | -0.015498 | -0.201218 | -0.126198 | -0.097202 | -0.312148 | -0.315093 | -0.131217 | | FengWuW2SHAPPY | 89 | FengWuAZN | 1918 | -0.22722 | -0.326246 | -0.1823 | -0.096451 | -0.061227- | 0.167039 | -0.146093 | -0.137272 | -0.337258 | -0.432199 | -0.242258 | -0.223447 | | WindBorneNordicS2S | 910 | WeatherMeshNordicS2S1 | 1820 | -0.182389 | -0.16356 | -0.139414 | -0.311509 | -0.12932 | -0.482476 | -0.2484 | -0.202235 | -0.202368 | -0.164369 | -0.105411 | -0.24503 | | HAPPYNordicS2S | 10 | AZNNordicS2S3 | 2022 | -0.23348 | -0.25154 | -0.245472 | -0.491493 | -0.295327 | -0.084514 | -0.09846 | -0.324337 | -0.257523 | -0.191576 | -0.281498 | -0.515594 |
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expand| NordicS2S | 10 | NordicS2S2 | 25 | -0.617 | -0.679 | -0.622 | -0.621 | -0.501 | -0.511 | -0.622 | -0.538 | -0.732 | -0.621 | -0.611 | -0.788 |
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| title | Forecast window 2 (days 26 to 32) |
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| title | Forecast window 2 (days 26 to 32) |
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| Team name | Team rank | Model name | Model rank | Global | Tropics | NHem. ExTro. | SHem. ExTro. | NHem. Polar | SHem. Polar | Europe | N. Amer. | S. Amer. | Africa | AsiaOceania | Oceania |
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| MicroEnsemble | 1 | MicroDuet | 1 | 0.055 | 0.086 | 0.019 | 0.053 | 0.029 | 0.034 | -0.004 | 0.063 | 0.053 | 0.079 | 0.043 | 0.079 | | MicroEnsemble | 1 | MicroDuetStillLearning | 12 | 0.054053 | 0.088083 | 0.01302 | 0.061041 | 0.022029 | 0.03304 | -0.015009 | 0.058056 | 0.061045 | 0.065092 | 0.038044 | 0.101076 | | MicroEnsemble | 1 | StillLearningHuracan | 28 | 0.05017 | 0.082019 | -0.015002 | 0.047042 | 0.022023 | 0.037023 | -0.017022 | 0.051056 | 0.051001 | -0.08022 | 0.039007 | 0.093033 | | MicroEnsembleCMAandFDU | 12 | HuracanFengshunAdjust | 73 | 0.01705 | 0.023096 | -0.00401 | 0.05008 | 0.017007 | 0.021012 | -0.031003 | 0.055043 | 0.006078 | -0.034114 | 0.006029 | 0.055041 | | CMAandFDU | 2 | FengshunAdjustFengshunHybrid | 34 | 0.043041 | 0.086072 | 0.009014 | 0.008018 | 0.00201 | 0.008021 | -0.0009 | 0.041056 | 0.06504 | 0.102075 | 0.024025 | 0.047024 | | CMAandFDU | 2 | FengshunHybridFengshun | 49 | 0.038007 | 0.071021 | 0.00901 | -0.02201 | 0.001 | -0.019051- | 0.005016 | 0.051044 | 0.03902 | 0.065003 | 0.018013 | -0.042056 | | CMAandFDULP | 23 | FengshunLPM | 95 | 0.004029 | 0.019045 | 0.005011 | -0.01012 | 0.005028 | -0.054027 | -0.013018 | 0.04057 | 0.011008 | -0.002035 | 0.006033 | -0.04021 | | LPAIFS | 34 | LPMAIFSgaia | 56 | 0.028 | 0.046045 | 0.00401 | -0.018003 | 0.022013 | 0.025028 | -0.033001 | 0.054057 | 0.024064 | 0.018017 | 0.027012 | 0.04011 | | AIFS | 4 | AIFSgaiaAIFShera | 67 | 0.022 | 0.043033 | 0.0009 | -0.004001 | 0.009018 | 0.016015- | 0.016002 | 0.051032 | 0.05406 | 0.015019 | 0.0012 | 0.015035 | | AIFS | 4 | AIFSheraAIFSthalassa | 810 | 0.015002 | -0.02401 | 0.001004 | -0.005002 | 0.014015 | 0.017027 | -0.006004 | 0.024037 | 0.052016 | -0.013069- | 0.003005 | 0.027004 | | AIFSscienceAI | 45 | AIFSthalassafindforecast | 1511 | -0.0040 | -0.017005 | -0.0030- | 0.004016 | 0.014013 | 0.025007 | -0.014008 | 0.034031 | -0.008023- | 0.084005 | -0.007015 | 0.014026 | | KITKanguscienceAI | 5 | KanguPlusPluszephyr | 1012 | -0.0 | -0.0 | -0.0 | -0.0 | -0.0 | -0.0 | -0.0 | -0.0 | -0.0 | -0.0 | -0.0 | -0.0 | | KITKanguscienceAI | 5 | KanguParametricPredictionngcm | 1012 | -0.0 | -0.0 | -0.0 | -0.0 | -0.0 | -0.0 | -0.0 | -0.0 | -0.0 | -0.0 | -0.0 | -0.0 | | scienceAIKITKangu | 56 | zephyrKanguPlusPlus | 1012 | -0.0 | -0.0 | -0.0 | -0.0 | -0.0 | -0.0 | -0.0 | -0.0 | -0.0 | -0.0 | -0.0 | -0.0 | | scienceAIKITKangu | 56 | ngcmKanguParametricPrediction | 1012 | -0.0 | -0.0 | -0.0 | -0.0 | -0.0 | -0.0 | -0.0 | -0.0 | -0.0 | -0.0 | -0.0 | -0.0 | | scienceAIKITKangu | 56 | findforecastKanguS2SEasyUQ | 1431 | -01.004149 | -01.009255 | -01.0060820 | -1.025063 | -0.0129470 | -1.004046 | -01.0010140 | -1.032149 | -01.022133 | -01.003462 | -01.021070 | -1.026125 | | KITKanguCliMA | 57 | KanguS2SEasyUQCliMAWeather2 | 3216 | -10.124138 | -10.244167 | -10.05188 | -10.073111 | -0.91057 | -0.99016 | -0.999151 | -10.143113 | -10.171161 | -10.411231 | -10.032169 | -10.132143 | | CliMA | 7 | CliMAWeather2CliMAWeather | 1620 | -0.156347 | -0.189395 | -0.212369 | -0.12383 | -0.065297 | -0.022222 | -0.17232 | -0.128187 | -0.18332 | -0.256476 | -0.193467 | -0.163448 | | CliMANewMeteor | 78 | CliMAWeatherBaseModel | 2017 | -0.35224 | -0.403415 | -0.371097 | -0.397094 | -0.287014 | -0.225017 | -0.258081 | -0.154099 | -0.35325 | -0.478252 | -0.483199 | -0.47165 | | FengWuW2SNewMeteor | 8 | FengWu2NewMet | 1725 | -0.186597 | -0.251731 | -0.243538 | -0.029519 | -0.09463 | -0.08363 | -0.128507 | -0.2516 | -0.125692 | -0.32642 | -0.297598 | -0.122525 | | FengWuW2SNewMeteor | 8 | FengWuExtraBaseModel | 1826 | -0.243607 | -0.371745 | -0.19544 | -0.066521 | -0.051467 | -0.12339 | -0.117513 | -0.185509 | -0.374708 | -0.478662 | -0.228611 | -0.238535 | | HAPPY | 9 | AZN | 1918 | -0.261251 | -0.318306 | -0.232 | -0.604669 | -0.25198 | 0.007017 | -0.182163 | -0.322275 | -0.429453 | -0.239229 | -0.205213 | -0.544483 | | WindBorne | 10 | WeatherMesh | 2119 | -0.352336 | -0.364327 | -0.193206 | -0.64585 | -0.271259 | -0.732748 | -0.243259 | -0.359 | -0.369287 | -0.239204 | -0.222213 | -0.628608 |
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Figures showing aggregated RPSSs for best-performing model from top 10 teams
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| title | Near-surface air temperature (tas), forecast window 1 (days 19 to 25) |
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| title | Near-surface air temperature (tas), forecast window 2 (days 26 to 32) |
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| title | Mean sea level pressure (mslp), forecast window 1 (days 19 to 25) |
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| title | Mean sea level pressure (mslp), forecast window 2 (days 26 to 32) |
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| title | Accumulated precipitation (pr), forecast window 1 (days 19 to 25) |
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| title | Accumulated precipitation (pr), forecast window 2 (days 26 to 32) |
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Figures showing evolution of skill scores
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| title | Weekly RPSSs for near-surface air temperature (tas), forecast window 1 (days 19 to 25) |
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| title | Weekly RPSSs for near-surface air temperature (tas), forecast window 2 (days 26 to 32) |
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| title | Weekly RPSSs for mean sea level pressure (mslp), forecast window 1 (days 19 to 25) |
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| title | Weekly RPSSs for mean sea level pressure (mslp), forecast window 2 (days 26 to 32) |
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| title | Weekly RPSSs for accumulated precipitation (pr), forecast window 1 (days 19 to 25) |
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| title | Weekly RPSSs for accumulated precipitation (pr), forecast window 2 (days 26 to 32) |
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| title | Weekly RPSSs for variable-averaged (average), forecast window 1 (days 19 to 25) |
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| title | Weekly RPSSs for variable-averaged (average), forecast window 2 (days 26 to 32) |
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| title | Aggregated RPSSs for near-surface air temperature (tas), forecast window 1 (days 19 to 25) |
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| title | Aggregated RPSSs for near-surface air temperature (tas), forecast window 2 (days 26 to 32) |
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| title | Aggregated RPSSs for mean sea level pressure (mslp), forecast window 1 (days 19 to 25) |
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| title | Aggregated RPSSs for mean sea level pressure (mslp), forecast window 2 (days 26 to 32) |
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| title | Aggregated RPSSs for accumulated precipitation (pr), forecast window 1 (days 19 to 25) |
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| title | Aggregated RPSSs for accumulated precipitation (pr), forecast window 2 (days 26 to 32) |
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| title | Aggregated RPSSs for variable-averaged (average), forecast window 1 (days 19 to 25) |
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| title | Aggregated RPSSs for variable-averaged (average), forecast window 2 (days 26 to 32) |
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Figures showing percentage of grid points with positive period-aggregated RPSSs
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| title | Near-surface air temperature (tas), forecast window 1 (days 19 to 25) |
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| title | Near-surface air temperature (tas), forecast window 2 (days 26 to 32) |
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| title | Mean sea level pressure (mslp), forecast window 1 (days 19 to 25) |
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| title | Mean sea level pressure (mslp), forecast window 2 (days 26 to 32) |
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| title | Accumulated precipitation (pr), forecast window 1 (days 19 to 25) |
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| title | Accumulated precipitation (pr), forecast window 2 (days 26 to 32) |
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Figures showing observed conditions with respect to defined ERA5 climatology
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| title | Near-surface air temperature (tas), forecast window 1 (days 19 to 25) |
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| title | Near-surface air temperature (tas), forecast window 2 (days 26 to 32) |
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| title | Mean sea level pressure (mslp), forecast window 1 (days 19 to 25) |
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| title | Mean sea level pressure (mslp), forecast window 2 (days 26 to 32) |
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| title | Accumulated precipitation (pr), forecast window 1 (days 19 to 25) |
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| title | Accumulated precipitation (pr), forecast window 2 (days 26 to 32) |
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