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 6 7 forecasts initialized between Thursday 14th August 2025 and Thursday 18th 25th September 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 | 0.073071 | 0.116108 | 0.032038 | 0.053061 | 0.024035 | 0.021018 | 0.01801 | 0.044052 | 0.082075 | 0.072068 | 0.069074 | 0.117128 | | MicroEnsemble | 1 | StillLearning | 32 | 0.067065 | 0.108098 | 0.029038 | 0.048053 | 0.018032 | 0.017023 | 0.019013 | 0.039048 | 0.071064 | 0.09709 | 0.059067 | 0.125121 | | MicroEnsemble | 1 | Huracan | 9 | 0.02703 | 0.032029 | 0.019026 | 0.035043 | 0.01403 | 0.008017 | 0.014002 | 0.03805 | -0.0060 | -0.036044 | 0.039046 | 0.043069 | | CMAandFDU | 2 | FengshunAdjustFengshunHybrid | 23 | 0.07058 | 0.14103 | 0.017023 | 0.016024- | 0.016017 | -0.02003 | -0.0130 | 0.038058 | 0.138091 | 0.105102 | 0.046 | 0.087052 | | CMAandFDU | 2 | FengshunHybridFengshunAdjust | 4 | 0.065058 | 0.119116 | 0.019015 | 0.02017 | -0.008009 | -0.002008- | 0.002009 | 0.06036 | 0.096122 | 0.101104 | 0.045038 | 0.044072 | | CMAandFDU | 2 | Fengshun | 1011 | 0.012001 | 0.031022 | -0.006007 | -0.008016 | 0.006001 | -0.053069 | 0.023012 | 0.023022 | 0.024016 | -0.01006 | 0.031015 | -0.05036 | | LP | 3 | LPM | 5 | 0.049045 | 0.074062 | 0.026034 | 0.042038 | 0.019033 | -0.0002 | -0.012006 | 0.045047 | 0.058049 | 0.035029 | 0.054062 | 0.052065 | | AIFS | 4 | AIFShera | 6 | 0.04 | 0.05048 | 0.031035 | 0.018023 | 0.024039 | 0.013002 | 0.019 | 0.029042 | 0.071068 | 0.027026 | 0.048051 | 0.019042 | | AIFS | 4 | AIFSgaia | 7 | 0.037038 | 0.065063 | 0.017021 | 0.00801 | 0.006022 | -0.014 | 0.007013 | 0.025037 | 0.052049 | 0.041045 | 0.036039 | -0.012026 | | AIFS | 4 | AIFSthalassa | 8 | 0.03603 | 0.058047 | 0.018019 | 0.004018 | 0.022027 | -0.002006- | 0.0005 | 0.022026 | 0.049042 | 0.02022 | 0.04039 | 0.012033 | | scienceAI | 5 | findforecast | 1110 | 0.001 | -0.001 | -0.003008 | 0.019025 | -0.009001 | 0.00701 | -0.015016 | 0.028032 | -0.006 | -0.014013 | -0.014018 | 0.031039 | | 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 | 3534 | -1.063074 | -1.154185 | -0.984987 | -1.074039 | -0.847866 | -10.001931 | -01.952017 | -10.0992 | -1.177242 | -1.1452 | -0.957992 | -1.321242 | | CliMA | 7 | CliMAWeather2 | 16 | -0.1312 | -0.14314 | -0.178153 | -0.114117 | -0.084067 | -0.003015 | -0.112104 | -0.133108 | -0.147148 | -0.203192 | -0.156135 | -0.104098 | | CliMA | 7 | CliMAWeather | 2221 | -0.25931 | -0.303359 | -0.256323 | -0.327387 | -0.232262 | -0.119145 | -0.224286 | -0.166149 | -0.287338 | -0.34396 | -0.332411 | -0.37449 | | WindBorneFengWuW2S | 8 | WeatherMeshFengWu2 | 17 | -0.166177 | -0.125197 | -0.163257 | -0.296019 | -0.14134 | -0.424015 | -0.2201 | -0.208126 | -0.151097 | -0.163312 | -0.123315 | -0.226131 | | FengWuW2S | 98 | FengWu2FengWu | 1819 | -0.191227 | -0.198326 | -0.28918 | -0.043096 | -0.158061 | -0.026167 | -0.219146 | -0.117137 | -0.083337 | -0.315432 | -0.358242 | -0.224223 | | FengWuW2SWindBorne | 9 | FengWuWeatherMesh | 1918 | -0.236182 | -0.33216 | -0.194139 | -0.111311 | -0.074129 | -0.172482 | -0.1462 | -0.139202 | -0.339202 | -0.427164 | -0.276105 | -0.27824 | | HAPPY | 10 | AZN | 20 | -0.244233 | -0.259251 | -0.255245 | -0.538491 | -0.299295 | 0.069084 | -0.072098 | -0.299324 | -0.303257 | -0.219191 | -0.316281 | -0.443515 |
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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 | Asia | Oceania |
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| MicroEnsemble | 1 | MicroDuet | 1 | 0.046054 | 0.081088 | -0.001013 | 0.063061 | 0.009022 | 0.034033 | -0.031015 | 0.052058 | 0.077061 | 0.06065 | 0.022038 | 0.086101 | | MicroEnsemble | 1 | StillLearning | 2 | 0.04305 | 0.078082 | -0.002015 | 0.052047 | 0.005022 | 0.033037 | -0.031017 | 0.044051 | 0.066051 | 0.07508 | 0.019039 | 0.089093 | | MicroEnsemble | 1 | Huracan | 7 | 0.009017 | 0.016023 | -0.019004 | 0.05405 | 0.003017 | 0.018021 | -0.043031 | 0.051055 | 0.024006 | -0.034- | 0.016006 | 0.031055 | | CMAandFDU | 2 | FengshunAdjust | 3 | 0.033043 | 0.071086 | 0.004009 | 0.01008 | -0.009002 | 0.004008 | -0.0030 | 0.036041 | 0.06065 | 0.084102 | 0.011024 | 0.038047 | | CMAandFDU | 2 | FengshunHybrid | 4 | 0.029038 | 0.059071 | 0.002009 | 0.024022 | -0.0160 | 0.018019 | -0.01005 | 0.048051 | 0.045039 | 0.058065 | -0.001018 | 0.023042 | | CMAandFDU | 2 | Fengshun | 139 | -0.006004 | 0.002019 | 0.003005 | -0.001 | -0.005 | -0.047054 | 0.009013 | 0.02404 | 0.001011 | -0.015002- | 0.002006 | -0.04604 | | LP | 3 | LPM | 5 | 0.022028 | 0.037046 | -0.004 | 0.026018 | 0.012022 | 0.023025 | -0.04033 | 0.056054 | 0.039024 | 0.021018 | 0.008027 | 0.01304 | | AIFS | 4 | AIFSgaia | 6 | 0.013022 | 0.032043 | -0.0030 | -0.012004- | 0.005009 | 0.008016 | -0.018016 | 0.051 | 0.054 | 0.001015- | 0.0150 | -0.008015 | | AIFS | 4 | AIFShera | 8 | 0.004015 | 0.013024 | -0.009001 | -0.015005- | 0.006014 | 0.01017 | -0.016006 | 0.024 | 0.054052 | -0.011013 | -0.023003 | 0.007027 | | AIFS | 4 | AIFSthalassa | 15 | -0.016004 | -0.035017 | -0.008003 | -0.01004 | 0.002014 | 0.023025 | -0.021014 | 0.037034 | 0.006008 | -0.113084 | -0.023007- | 0.013014 | | KITKangu | 5 | KanguPlusPlus | 910 | -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 | 5 | KanguParametricPrediction | 910 | -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 | zephyr | 910 | -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 | 910 | -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 | findforecast | 14 | -0.01004 | -0.018009 | -0.009006 | 0.028025 | 0.004012 | 0.0004 | -0.01001 | 0.027032 | -0.024022 | -0.015003 | -0.025021 | 0.019026 | | KITKangu | 5 | KanguS2SEasyUQ | 3432 | -1.086124 | -1.202244 | -1.00405 | -1.025073 | -0.8491 | -10.00399 | -10.014999 | -1.106143 | -1.168171 | -1.326411 | -01.955032 | -1.132 | | CliMA | 7 | CliMAWeather2 | 16 | -0.186156 | -0.227189 | -0.245212 | -0.14512 | -0.086065 | -0.013022 | -0.20617 | -0.152128 | -0.2218 | -0.304256 | -0.226193 | -0.187163 | | CliMA | 7 | CliMAWeather | 2120 | -0.3335 | -0.389403 | -0.333371 | -0.368397 | -0.286287 | -0.2225 | -0.318258 | -0.109154 | -0.35535 | -0.49478 | -0.435483 | -0.41647 | | FengWuW2S | 8 | FengWu2 | 17 | -0.199186 | -0.261251 | -0.265243 | 0.029 | -0.10509 | 0.0908 | -0.108128 | -0.2262 | -0.117125 | -0.3332 | -0.331297 | -0.178122 | | FengWuW2S | 8 | FengWu | 18 | -0.255243 | -0.38371 | -0.205 | -0.065 | -0.064 | -0.136 | -0.109 | -0.203 | -0.37 | -0.491 | -0.252 | -0.269 | | HAPPY | 9 | AZN | 19 | -0.291 | -0.328 | -0.268 | -0.688 | -0.338 | 0.021 | -0.214 | -0.396 | -0.468 | -0.227 | -0.254 | -0.569 | | NordicS2S | 10 | NordicS2S1 | 20 | -0.305 | -0.248 | -0.33419 | -0.343066 | -0.387051 | -0.335123 | -0.48117 | -0.24185 | -0.289374 | -0.28478 | -0.333228 | -0.306238 | | NordicS2SHAPPY | 109 | NordicS2S3AZN | 2519 | -0.492261 | -0.577318 | -0.385232 | -0.298604 | -0.45125- | 0.59007 | -0.534182 | -0.267322 | -0.522429 | -0.694239 | -0.483205 | -0.559544 | | NordicS2SWindBorne | 10 | NordicS2S2WeatherMesh | 2721 | -0.591352 | -0.615364 | -0.609193 | -0.59164 | -0.513271 | -0.603732 | -0.727243 | -0.465359 | -0.717369 | -0.631239 | -0.573222 | -0.545628 |
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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 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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