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 3 forecasts initialized between Thursday 14th August 2025 and Thursday 28th 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 |
|---|
| MicroEnsemble | 1 | MicroDuet | 1 | 0.071 | 0.106 | 0.05 | 0.039 | 0.029 | 0.02 | 0.072 | 0.019 | 0.057 | 0.065 | 0.094 | 0.105 | | MicroEnsemble | 1 | StillLearning | 2 | 0.069 | 0.103 | 0.054 | 0.034 | 0.03 | 0.01 | 0.078 | 0.012 | 0.055 | 0.087 | 0.092 | 0.102 | | MicroEnsemble | 1 | Huracan | 9 | 0.032 | 0.031 | 0.041 | 0.029 | 0.015 | 0.014 | 0.065 | 0.015 | -0.018 | -0.039 | 0.063 | 0.057 | | CMAandFDU | 2 | FengshunAdjust | 3 | 0.066 | 0.129 | 0.033 | 0.012 | 0.001 | -0.049 | 0.046 | 0.022 | 0.118 | 0.075 | 0.082 | 0.117 | | CMAandFDU | 2 | FengshunHybrid | 4 | 0.065 | 0.117 | 0.037 | 0.007 | 0.016 | -0.021 | 0.052 | 0.031 | 0.044 | 0.095 | 0.091 | 0.092 | | CMAandFDU | 2 | Fengshun | 10 | 0.015 | 0.049 | -0.001 | -0.043 | 0.008 | -0.048 | 0.032 | -0.035 | 0.009 | -0.01 | 0.065 | 0.067 | | LP | 3 | LPM | 5 | 0.053 | 0.076 | 0.049 | 0.051 | 0.018 | -0.027 | 0.048 | 0.021 | 0.035 | 0.036 | 0.087 | 0.062 | | AIFS | 4 | AIFSgaia | 6 | 0.047 | 0.073 | 0.04 | 0.004 | 0.021 | -0.023 | 0.078 | -0.032 | 0.032 | 0.065 | 0.082 | 0.008 | | AIFS | 4 | AIFShera | 7 | 0.044 | 0.059 | 0.038 | -0.002 | 0.02 | -0.005 | 0.049 | -0.034 | 0.058 | 0.031 | 0.082 | 0.061 | | AIFS | 4 | AIFSthalassa | 8 | 0.035 | 0.064 | 0.015 | -0.013 | 0.018 | -0.04 | 0.019 | -0.038 | 0.012 | 0.038 | 0.068 | 0.02 | | 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 | -1.324 | -1.43 | -1.221 | -1.327 | -1.157 | -1.195 | -1.167 | -1.133 | -1.423 | -1.436 | -1.263 | -1.814 | | CliMA | 7 | CliMAWeather | 16 | -0.094 | -0.118 | -0.107 | -0.146 | -0.077 | 0.031 | -0.039 | -0.114 | -0.123 | -0.153 | -0.121 | -0.088 | | CliMA | 7 | CliMAWeather2 | 17 | -0.095 | -0.109 | -0.109 | -0.118 | -0.095 | 0.028 | -0.07 | -0.136 | -0.102 | -0.161 | -0.095 | -0.039 | | WindBorne | 8 | WeatherMesh | 18 | -0.203 | -0.181 | -0.21 | -0.26 | -0.21 | -0.301 | -0.213 | -0.229 | -0.204 | -0.24 | -0.197 | -0.228 | | FengWuW2S | 9 | FengWu2 | 19 | -0.247 | -0.163 | -0.434 | -0.085 | -0.346 | -0.123 | -0.435 | -0.145 | -0.073 | -0.294 | -0.506 | -0.175 | | FengWuW2S | 9 | FengWu | 21 | -0.287 | -0.36 | -0.289 | -0.149 | -0.154 | -0.176 | -0.245 | -0.195 | -0.381 | -0.463 | -0.378 | -0.273 | | NordicS2S | 10 | NordicS2S1 | 20 | -0.249 | -0.173 | -0.285 | -0.324 | -0.164 | -0.409 | -0.193 | -0.227 | -0.197 | -0.228 | -0.153 | -0.279 | | NordicS2S | 10 | NordicS2S3 | 23 | -0.39 | -0.402 | -0.376 | -0.402 | -0.313 | -0.495 | -0.29 | -0.359 | -0.5 | -0.456 | -0.296 | -0.461 | | NordicS2S | 10 | NordicS2S2 | 28 | -0.556 | -0.625 | -0.547 | -0.441 | -0.393 | -0.457 | -0.269 | -0.678 | -0.725 | -0.601 | -0.451 | -0.636 |
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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 |
|---|
| CMAandFDU | 1 | FengshunAdjust | 1 | 0.057 | 0.107 | 0.025 | 0.008 | -0.002 | 0.016 | 0.008 | 0.048 | 0.083 | 0.083 | 0.049 | 0.104 | | CMAandFDU | 1 | FengshunHybrid | 4 | 0.046 | 0.077 | 0.034 | 0.013 | -0.015 | 0.028 | 0.018 | 0.037 | 0.046 | 0.045 | 0.046 | 0.061 | | CMAandFDU | 1 | Fengshun | 9 | 0.003 | 0.01 | 0.017 | 0.015 | 0.005 | -0.029 | 0.001 | 0.007 | -0.012 | -0.025 | 0.034 | 0.003 | | MicroEnsemble | 2 | MicroDuet | 2 | 0.054 | 0.092 | 0.018 | 0.035 | 0.015 | 0.016 | 0.029 | 0.029 | 0.093 | 0.054 | 0.039 | 0.101 | | MicroEnsemble | 2 | StillLearning | 3 | 0.053 | 0.094 | 0.022 | 0.02 | 0.013 | 0.002 | 0.038 | 0.013 | 0.084 | 0.079 | 0.041 | 0.112 | | MicroEnsemble | 2 | Huracan | 7 | 0.011 | 0.021 | -0.001 | 0.026 | 0.005 | -0.019 | 0.012 | 0.02 | 0.031 | -0.037 | 0.001 | 0.026 | | LP | 3 | LPM | 5 | 0.033 | 0.054 | 0.02 | 0.003 | 0.015 | -0.008 | 0.013 | 0.046 | 0.053 | 0.014 | 0.039 | 0.017 | | AIFS | 4 | AIFSgaia | 6 | 0.024 | 0.04 | 0.025 | -0.013 | -0.003 | 0.004 | 0.004 | 0.033 | 0.078 | -0.007 | 0.023 | -0.02 | | AIFS | 4 | AIFShera | 8 | 0.009 | 0.014 | 0.005 | -0.008 | -0.002 | 0.013 | -0.007 | -0.006 | 0.072 | -0.023 | 0.005 | 0.001 | | AIFS | 4 | AIFSthalassa | 15 | -0.007 | -0.028 | 0.013 | -0.034 | -0.002 | 0.024 | -0.008 | 0.018 | 0.017 | -0.126 | 0.011 | -0.048 | | KITKangu | 5 | KanguPlusPlus | 10 | -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 | 10 | -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 | 10 | -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 | 10 | -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 | 10 | -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 | -1.38 | -1.494 | -1.32 | -1.22 | -1.016 | -1.336 | -1.229 | -1.431 | -1.496 | -1.559 | -1.197 | -1.388 | | CliMA | 7 | CliMAWeather | 16 | -0.179 | -0.226 | -0.173 | -0.232 | -0.132 | -0.072 | -0.144 | -0.11 | -0.237 | -0.352 | -0.228 | -0.111 | | CliMA | 7 | CliMAWeather2 | 17 | -0.201 | -0.23 | -0.255 | -0.232 | -0.114 | -0.064 | -0.131 | -0.153 | -0.224 | -0.352 | -0.267 | -0.195 | | NordicS2S | 8 | NordicS2S1 | 18 | -0.26 | -0.27 | -0.225 | -0.31 | -0.172 | -0.285 | -0.312 | -0.158 | -0.325 | -0.406 | -0.204 | -0.272 | | NordicS2S | 8 | NordicS2S3 | 25 | -0.507 | -0.69 | -0.283 | -0.378 | -0.256 | -0.524 | -0.329 | -0.348 | -0.696 | -0.8 | -0.346 | -0.718 | | NordicS2S | 8 | NordicS2S2 | 28 | -0.574 | -0.648 | -0.56 | -0.643 | -0.339 | -0.498 | -0.521 | -0.531 | -0.738 | -0.69 | -0.499 | -0.597 | | FengWuW2S | 9 | FengWu2 | 19 | -0.266 | -0.266 | -0.425 | 0.003 | -0.225 | 0.042 | -0.212 | -0.257 | -0.127 | -0.33 | -0.493 | -0.304 | | FengWuW2S | 9 | FengWu | 20 | -0.312 | -0.419 | -0.313 | -0.08 | -0.138 | -0.123 | -0.127 | -0.271 | -0.429 | -0.558 | -0.374 | -0.294 | | HAPPY | 10 | AZN | 21 | -0.39 | -0.46 | -0.384 | -0.854 | -0.313 | -0.028 | -0.409 | -0.354 | -0.653 | -0.358 | -0.352 | -0.728 |
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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
