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 forecasts initialized between Thursday 14th August 2025 and Thursday 18th September 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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| MicroEnsemble | 1 | MicroDuet | 1 | 0.073 | 0.116 | 0.032 | 0.053 | 0.024 | 0.021 | 0.018 | 0.044 | 0.082 | 0.072 | 0.069 | 0.117 | | MicroEnsemble | 1 | StillLearning | 3 | 0.067 | 0.108 | 0.029 | 0.048 | 0.018 | 0.017 | 0.019 | 0.039 | 0.071 | 0.097 | 0.059 | 0.125 | | MicroEnsemble | 1 | Huracan | 9 | 0.027 | 0.032 | 0.019 | 0.035 | 0.014 | 0.008 | 0.014 | 0.038 | 0.006 | -0.036 | 0.039 | 0.043 | | CMAandFDU | 2 | FengshunAdjust | 2 | 0.07 | 0.14 | 0.017 | 0.016 | -0.016 | -0.02 | 0.013 | 0.038 | 0.138 | 0.105 | 0.046 | 0.087 | | CMAandFDU | 2 | FengshunHybrid | 4 | 0.065 | 0.119 | 0.019 | 0.02 | 0.008 | 0.002 | -0.002 | 0.06 | 0.096 | 0.101 | 0.045 | 0.044 | | CMAandFDU | 2 | Fengshun | 10 | 0.012 | 0.031 | 0.006 | -0.008 | 0.006 | -0.053 | 0.023 | 0.023 | 0.024 | -0.01 | 0.031 | -0.05 | | LP | 3 | LPM | 5 | 0.049 | 0.074 | 0.026 | 0.042 | 0.019 | -0.0 | -0.012 | 0.045 | 0.058 | 0.035 | 0.054 | 0.052 | | AIFS | 4 | AIFShera | 6 | 0.04 | 0.05 | 0.031 | 0.018 | 0.024 | 0.013 | 0.019 | 0.029 | 0.071 | 0.027 | 0.048 | 0.019 | | AIFS | 4 | AIFSgaia | 7 | 0.037 | 0.065 | 0.017 | 0.008 | 0.006 | -0.014 | 0.007 | 0.025 | 0.052 | 0.041 | 0.036 | -0.012 | | AIFS | 4 | AIFSthalassa | 8 | 0.036 | 0.058 | 0.018 | 0.004 | 0.022 | 0.002 | -0.0 | 0.022 | 0.049 | 0.02 | 0.04 | 0.012 | | scienceAI | 5 | findforecast | 11 | 0.001 | -0.001 | -0.003 | 0.019 | -0.009 | 0.007 | -0.015 | 0.028 | -0.006 | -0.014 | -0.014 | 0.031 | | 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 | 35 | -1.063 | -1.154 | -0.984 | -1.074 | -0.847 | -1.001 | -0.952 | -1.0 | -1.177 | -1.145 | -0.957 | -1.321 | | CliMA | 7 | CliMAWeather2 | 16 | -0.13 | -0.143 | -0.178 | -0.114 | -0.084 | -0.003 | -0.112 | -0.133 | -0.147 | -0.203 | -0.156 | -0.104 | | CliMA | 7 | CliMAWeather | 22 | -0.259 | -0.303 | -0.256 | -0.327 | -0.232 | -0.119 | -0.224 | -0.166 | -0.287 | -0.34 | -0.332 | -0.37 | | WindBorne | 8 | WeatherMesh | 17 | -0.166 | -0.125 | -0.163 | -0.296 | -0.14 | -0.424 | -0.2 | -0.208 | -0.151 | -0.163 | -0.123 | -0.226 | | FengWuW2S | 9 | FengWu2 | 18 | -0.191 | -0.198 | -0.289 | -0.043 | -0.158 | -0.026 | -0.219 | -0.117 | -0.083 | -0.315 | -0.358 | -0.224 | | FengWuW2S | 9 | FengWu | 19 | -0.236 | -0.332 | -0.194 | -0.111 | -0.074 | -0.172 | -0.146 | -0.139 | -0.339 | -0.427 | -0.276 | -0.278 | | HAPPY | 10 | AZN | 20 | -0.244 | -0.259 | -0.255 | -0.538 | -0.299 | 0.069 | -0.072 | -0.299 | -0.303 | -0.219 | -0.316 | -0.443 |
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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 | MicroDuet | 1 | 0.046 | 0.081 | -0.001 | 0.063 | 0.009 | 0.034 | -0.031 | 0.052 | 0.077 | 0.06 | 0.022 | 0.086 | | MicroEnsemble | 1 | StillLearning | 2 | 0.043 | 0.078 | -0.002 | 0.052 | 0.005 | 0.033 | -0.031 | 0.044 | 0.066 | 0.075 | 0.019 | 0.089 | | MicroEnsemble | 1 | Huracan | 7 | 0.009 | 0.016 | -0.019 | 0.054 | 0.003 | 0.018 | -0.043 | 0.051 | 0.024 | -0.034 | -0.016 | 0.031 | | CMAandFDU | 2 | FengshunAdjust | 3 | 0.033 | 0.071 | 0.004 | 0.01 | -0.009 | 0.004 | -0.003 | 0.036 | 0.06 | 0.084 | 0.011 | 0.038 | | CMAandFDU | 2 | FengshunHybrid | 4 | 0.029 | 0.059 | 0.002 | 0.024 | -0.016 | 0.018 | -0.01 | 0.048 | 0.045 | 0.058 | -0.001 | 0.023 | | CMAandFDU | 2 | Fengshun | 13 | -0.006 | 0.002 | 0.003 | 0.0 | -0.005 | -0.047 | 0.009 | 0.024 | 0.001 | -0.015 | -0.002 | -0.046 | | LP | 3 | LPM | 5 | 0.022 | 0.037 | -0.004 | 0.026 | 0.012 | 0.023 | -0.04 | 0.056 | 0.039 | 0.021 | 0.008 | 0.013 | | AIFS | 4 | AIFSgaia | 6 | 0.013 | 0.032 | -0.003 | -0.012 | -0.005 | 0.008 | -0.018 | 0.051 | 0.054 | 0.001 | -0.015 | -0.008 | | AIFS | 4 | AIFShera | 8 | 0.004 | 0.013 | -0.009 | -0.015 | -0.006 | 0.01 | -0.016 | 0.024 | 0.054 | -0.011 | -0.023 | 0.007 | | AIFS | 4 | AIFSthalassa | 15 | -0.016 | -0.035 | -0.008 | -0.01 | 0.002 | 0.023 | -0.021 | 0.037 | 0.006 | -0.113 | -0.023 | -0.013 | | KITKangu | 5 | KanguPlusPlus | 9 | -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 | 9 | -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 | 9 | -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 | 9 | -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 | -0.01 | -0.018 | -0.009 | 0.028 | 0.004 | 0.0 | -0.01 | 0.027 | -0.024 | -0.015 | -0.025 | 0.019 | | KITKangu | 5 | KanguS2SEasyUQ | 34 | -1.086 | -1.202 | -1.004 | -1.025 | -0.84 | -1.003 | -1.014 | -1.106 | -1.168 | -1.326 | -0.955 | -1.132 | | CliMA | 7 | CliMAWeather2 | 16 | -0.186 | -0.227 | -0.245 | -0.145 | -0.086 | -0.013 | -0.206 | -0.152 | -0.22 | -0.304 | -0.226 | -0.187 | | CliMA | 7 | CliMAWeather | 21 | -0.33 | -0.389 | -0.333 | -0.368 | -0.286 | -0.2 | -0.318 | -0.109 | -0.355 | -0.49 | -0.435 | -0.416 | | FengWuW2S | 8 | FengWu2 | 17 | -0.199 | -0.261 | -0.265 | 0.029 | -0.105 | 0.09 | -0.108 | -0.226 | -0.117 | -0.33 | -0.331 | -0.178 | | FengWuW2S | 8 | FengWu | 18 | -0.255 | -0.38 | -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 | -0.334 | -0.343 | -0.387 | -0.335 | -0.48 | -0.24 | -0.289 | -0.28 | -0.333 | -0.306 | | NordicS2S | 10 | NordicS2S3 | 25 | -0.492 | -0.577 | -0.385 | -0.298 | -0.451 | -0.59 | -0.534 | -0.267 | -0.522 | -0.694 | -0.483 | -0.559 | | NordicS2S | 10 | NordicS2S2 | 27 | -0.591 | -0.615 | -0.609 | -0.591 | -0.513 | -0.603 | -0.727 | -0.465 | -0.717 | -0.631 | -0.573 | -0.545 |
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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
