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 2 forecasts initialized between Thursday 14th August 2025 and Thursday 21st August 2025 (inclusive). For a detailed description of the outputs, please refer to the section's overview.
Regional skill score files
All regional RPSSs are available to download via the following link:
SON 2025 Regional RPSSs (Excel format)
Regional scores for the top 10 teams of global, period-aggregated, variable-averaged RPSSs
| 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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| CMAandFDU | 1 | FengshunAdjust | 1 | 0.086 | 0.169 | 0.023 | 0.006 | 0.002 | -0.059 | 0.036 | 0.006 | 0.15 | 0.119 | 0.105 | 0.13 | | CMAandFDU | 1 | FengshunHybrid | 2 | 0.072 | 0.132 | 0.026 | -0.0 | 0.028 | -0.033 | 0.038 | 0.008 | 0.035 | 0.123 | 0.116 | 0.057 | | CMAandFDU | 1 | Fengshun | 10 | 0.016 | 0.055 | -0.006 | -0.075 | 0.032 | -0.067 | 0.021 | -0.029 | 0.02 | -0.037 | 0.083 | 0.06 | | MicroEnsemble | 2 | StillLearning | 3 | 0.072 | 0.104 | 0.046 | 0.027 | 0.03 | 0.024 | 0.067 | -0.01 | 0.038 | 0.096 | 0.105 | 0.071 | | MicroEnsemble | 2 | MicroDuet | 4 | 0.072 | 0.105 | 0.041 | 0.026 | 0.031 | 0.03 | 0.061 | -0.0 | 0.037 | 0.063 | 0.106 | 0.077 | | MicroEnsemble | 2 | Huracan | 9 | 0.036 | 0.034 | 0.039 | 0.006 | 0.018 | 0.022 | 0.056 | -0.009 | -0.038 | -0.035 | 0.083 | 0.033 | | AIFS | 3 | AIFSgaia | 5 | 0.065 | 0.097 | 0.032 | 0.011 | 0.043 | -0.009 | 0.079 | -0.069 | 0.03 | 0.094 | 0.113 | 0.037 | | AIFS | 3 | AIFShera | 6 | 0.065 | 0.082 | 0.044 | 0.001 | 0.032 | 0.01 | 0.077 | -0.053 | 0.083 | 0.045 | 0.106 | 0.078 | | AIFS | 3 | AIFSthalassa | 8 | 0.048 | 0.082 | 0.013 | -0.038 | 0.028 | -0.055 | 0.026 | -0.064 | 0.005 | 0.067 | 0.096 | -0.024 | | LP | 4 | LPM | 7 | 0.061 | 0.08 | 0.055 | 0.038 | 0.015 | -0.003 | 0.065 | -0.002 | 0.033 | 0.041 | 0.103 | 0.031 | | KITKangu | 5 | KanguPlusPlus | 11 | -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 | 11 | -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 | 11 | -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 | 11 | -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 | 11 | -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 | KanguS2SEasyUQ | 38 | -1.28 | -1.373 | -1.141 | -1.284 | -1.169 | -1.235 | -1.095 | -1.036 | -1.403 | -1.37 | -1.222 | -1.826 | | CliMA | 7 | CliMAWeather2 | 16 | -0.075 | -0.092 | -0.09 | -0.192 | -0.038 | 0.046 | -0.066 | -0.109 | -0.144 | -0.159 | -0.065 | -0.012 | | CliMA | 7 | CliMAWeather | 17 | -0.078 | -0.095 | -0.095 | -0.197 | -0.042 | 0.045 | -0.069 | -0.113 | -0.143 | -0.163 | -0.07 | -0.019 | | WindBorne | 8 | WeatherMesh | 18 | -0.179 | -0.128 | -0.237 | -0.189 | -0.207 | -0.337 | -0.244 | -0.244 | -0.139 | -0.196 | -0.176 | -0.183 | | FengWuW2S | 9 | FengWu2 | 19 | -0.275 | -0.172 | -0.467 | -0.154 | -0.396 | -0.224 | -0.509 | -0.109 | -0.056 | -0.344 | -0.564 | -0.245 | | FengWuW2S | 9 | FengWu | 22 | -0.305 | -0.381 | -0.323 | -0.236 | -0.181 | -0.103 | -0.308 | -0.199 | -0.416 | -0.512 | -0.412 | -0.3 | | NordicS2S | 10 | NordicS2S1 | 20 | -0.285 | -0.208 | -0.372 | -0.324 | -0.185 | -0.39 | -0.241 | -0.326 | -0.226 | -0.257 | -0.198 | -0.399 | | NordicS2S | 10 | NordicS2S3 | 23 | -0.421 | -0.397 | -0.506 | -0.378 | -0.379 | -0.414 | -0.409 | -0.445 | -0.516 | -0.42 | -0.387 | -0.54 | | NordicS2S | 10 | NordicS2S2 | 29 | -0.573 | -0.634 | -0.611 | -0.445 | -0.418 | -0.387 | -0.308 | -0.715 | -0.82 | -0.534 | -0.535 | -0.66 |
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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 |
|---|
| MicroEnsemble | 1 | StillLearning | 1 | 0.054 | 0.094 | 0.026 | 0.033 | 0.044 | 0.001 | 0.048 | 0.026 | 0.089 | 0.068 | 0.048 | 0.123 | | MicroEnsemble | 1 | MicroDuet | 2 | 0.052 | 0.087 | 0.02 | 0.053 | 0.039 | 0.019 | 0.038 | 0.047 | 0.101 | 0.034 | 0.043 | 0.107 | | MicroEnsemble | 1 | Huracan | 7 | 0.016 | 0.022 | 0.002 | 0.057 | 0.031 | -0.005 | 0.032 | 0.04 | 0.047 | -0.052 | 0.0 | 0.065 | | CMAandFDU | 2 | FengshunHybrid | 3 | 0.048 | 0.075 | 0.036 | 0.022 | 0.009 | 0.025 | 0.045 | 0.03 | 0.045 | 0.063 | 0.056 | 0.069 | | CMAandFDU | 2 | FengshunAdjust | 4 | 0.046 | 0.08 | 0.035 | 0.019 | 0.017 | 0.014 | 0.039 | 0.048 | 0.065 | 0.051 | 0.053 | 0.131 | | CMAandFDU | 2 | Fengshun | 9 | 0.005 | 0.018 | 0.01 | 0.016 | 0.013 | -0.031 | 0.02 | -0.017 | -0.002 | -0.014 | 0.032 | 0.069 | | LP | 3 | LPM | 5 | 0.036 | 0.065 | 0.018 | 0.018 | 0.032 | -0.026 | 0.024 | 0.047 | 0.051 | 0.031 | 0.043 | 0.058 | | AIFS | 4 | AIFSgaia | 6 | 0.025 | 0.046 | 0.004 | 0.024 | 0.012 | 0.009 | 0.031 | -0.004 | 0.083 | 0.016 | 0.018 | 0.033 | | AIFS | 4 | AIFShera | 8 | 0.012 | 0.023 | -0.004 | 0.023 | 0.015 | -0.013 | 0.036 | -0.038 | 0.075 | -0.003 | 0.007 | 0.051 | | AIFS | 4 | AIFSthalassa | 10 | 0.002 | -0.01 | 0.007 | -0.004 | 0.012 | 0.012 | 0.02 | 0.002 | 0.011 | -0.061 | 0.009 | -0.011 | | KITKangu | 5 | KanguPlusPlus | 11 | -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 | 11 | -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 | 11 | -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 | 11 | -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 | 11 | -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 | KanguS2SEasyUQ | 38 | -1.419 | -1.522 | -1.373 | -1.09 | -1.149 | -1.365 | -1.237 | -1.406 | -1.511 | -1.626 | -1.306 | -1.299 | | CliMA | 7 | CliMAWeather | 16 | -0.173 | -0.221 | -0.203 | -0.181 | -0.094 | -0.026 | -0.101 | -0.103 | -0.155 | -0.391 | -0.247 | -0.106 | | CliMA | 7 | CliMAWeather2 | 17 | -0.181 | -0.227 | -0.214 | -0.192 | -0.1 | -0.033 | -0.113 | -0.111 | -0.171 | -0.408 | -0.251 | -0.105 | | NordicS2S | 8 | NordicS2S1 | 18 | -0.25 | -0.279 | -0.192 | -0.2 | -0.153 | -0.319 | -0.176 | -0.146 | -0.284 | -0.444 | -0.195 | -0.228 | | NordicS2S | 8 | NordicS2S3 | 24 | -0.473 | -0.701 | -0.245 | -0.14 | -0.275 | -0.396 | -0.214 | -0.431 | -0.649 | -0.821 | -0.338 | -0.485 | | NordicS2S | 8 | NordicS2S2 | 29 | -0.581 | -0.69 | -0.602 | -0.498 | -0.299 | -0.355 | -0.416 | -0.628 | -0.671 | -0.849 | -0.465 | -0.378 | | FengWuW2S | 9 | FengWu2 | 19 | -0.255 | -0.224 | -0.437 | 0.032 | -0.256 | 0.018 | -0.264 | -0.212 | -0.066 | -0.257 | -0.529 | -0.247 | | FengWuW2S | 9 | FengWu | 20 | -0.305 | -0.408 | -0.315 | -0.035 | -0.138 | -0.107 | -0.174 | -0.214 | -0.411 | -0.517 | -0.398 | -0.3 | | HAPPY | 10 | AZN | 21 | -0.418 | -0.493 | -0.407 | -0.788 | -0.226 | -0.179 | -0.326 | -0.478 | -0.761 | -0.353 | -0.311 | -0.654 |
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Figures showing aggregated RPSSs for best-performing model from top 10 teams
Figures showing percentage of grid points with positive period-aggregated RPSSs
Figures showing observed conditions with respect to defined ERA5 climatology
