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 forecasts initialized between Thursday 14th August 2025 and Thursday 4th 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 |
|---|
| CMAandFDU | 1 | FengshunAdjust | 1 | 0.072 | 0.141 | 0.028 | 0.001 | -0.013 | -0.033 | 0.023 | 0.029 | 0.13 | 0.1 | 0.064 | 0.114 | | CMAandFDU | 1 | FengshunHybrid | 3 | 0.068 | 0.122 | 0.036 | -0.002 | 0.013 | -0.013 | 0.019 | 0.041 | 0.066 | 0.096 | 0.076 | 0.092 | | CMAandFDU | 1 | Fengshun | 10 | 0.019 | 0.049 | 0.001 | -0.026 | 0.011 | -0.046 | 0.01 | -0.008 | 0.024 | -0.002 | 0.056 | 0.031 | | MicroEnsemble | 2 | MicroDuet | 2 | 0.072 | 0.11 | 0.046 | 0.033 | 0.028 | 0.015 | 0.049 | 0.032 | 0.057 | 0.069 | 0.078 | 0.117 | | MicroEnsemble | 2 | StillLearning | 4 | 0.068 | 0.103 | 0.048 | 0.027 | 0.023 | 0.007 | 0.058 | 0.026 | 0.052 | 0.095 | 0.071 | 0.113 | | MicroEnsemble | 2 | Huracan | 9 | 0.029 | 0.031 | 0.035 | 0.013 | 0.013 | 0.003 | 0.034 | 0.027 | -0.024 | -0.032 | 0.049 | 0.047 | | LP | 3 | LPM | 5 | 0.053 | 0.074 | 0.048 | 0.043 | 0.02 | -0.015 | 0.022 | 0.042 | 0.04 | 0.026 | 0.071 | 0.068 | | AIFS | 4 | AIFSgaia | 6 | 0.047 | 0.073 | 0.043 | -0.013 | 0.019 | -0.03 | 0.053 | 0.004 | 0.041 | 0.053 | 0.072 | -0.014 | | AIFS | 4 | AIFShera | 7 | 0.045 | 0.059 | 0.047 | -0.014 | 0.021 | -0.003 | 0.026 | 0.004 | 0.056 | 0.022 | 0.08 | 0.035 | | AIFS | 4 | AIFSthalassa | 8 | 0.036 | 0.057 | 0.03 | -0.034 | 0.023 | -0.022 | 0.013 | -0.003 | 0.019 | 0.023 | 0.067 | -0.008 | | scienceAI | 5 | findforecast | 11 | 0.003 | -0.0 | 0.006 | 0.001 | 0.001 | 0.012 | -0.01 | 0.01 | -0.002 | -0.002 | 0.004 | -0.002 | | 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 | -1.306 | -1.415 | -1.228 | -1.346 | -1.062 | -1.168 | -1.159 | -1.208 | -1.46 | -1.342 | -1.225 | -1.72 | | CliMA | 7 | CliMAWeather2 | 16 | -0.135 | -0.143 | -0.164 | -0.157 | -0.113 | -0.02 | -0.098 | -0.161 | -0.151 | -0.189 | -0.149 | -0.096 | | CliMA | 7 | CliMAWeather | 17 | -0.159 | -0.194 | -0.157 | -0.265 | -0.122 | -0.043 | -0.127 | -0.172 | -0.208 | -0.228 | -0.17 | -0.197 | | WindBorne | 8 | WeatherMesh | 18 | -0.196 | -0.161 | -0.206 | -0.295 | -0.192 | -0.36 | -0.2 | -0.223 | -0.185 | -0.189 | -0.202 | -0.205 | | FengWuW2S | 9 | FengWu2 | 19 | -0.223 | -0.176 | -0.367 | -0.084 | -0.262 | -0.087 | -0.326 | -0.119 | -0.088 | -0.3 | -0.443 | -0.214 | | FengWuW2S | 9 | FengWu | 21 | -0.264 | -0.34 | -0.247 | -0.156 | -0.128 | -0.172 | -0.19 | -0.187 | -0.367 | -0.444 | -0.334 | -0.25 | | NordicS2S | 10 | NordicS2S1 | 20 | -0.232 | -0.159 | -0.259 | -0.321 | -0.192 | -0.424 | -0.237 | -0.201 | -0.227 | -0.185 | -0.168 | -0.236 | | NordicS2S | 10 | NordicS2S3 | 23 | -0.362 | -0.383 | -0.344 | -0.389 | -0.266 | -0.495 | -0.298 | -0.297 | -0.469 | -0.444 | -0.29 | -0.455 | | NordicS2S | 10 | NordicS2S2 | 26 | -0.525 | -0.576 | -0.494 | -0.502 | -0.384 | -0.53 | -0.319 | -0.614 | -0.695 | -0.485 | -0.418 | -0.664 |
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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 | 0.058 | 0.109 | 0.015 | 0.006 | -0.007 | 0.017 | -0.012 | 0.049 | 0.088 | 0.088 | 0.031 | 0.082 | | CMAandFDU | 1 | FengshunHybrid | 4 | 0.043 | 0.079 | 0.014 | 0.01 | -0.022 | 0.022 | -0.02 | 0.039 | 0.053 | 0.053 | 0.023 | 0.037 | | CMAandFDU | 1 | Fengshun | 7 | 0.009 | 0.023 | 0.016 | 0.007 | 0.002 | -0.034 | -0.001 | 0.015 | 0.003 | -0.012 | 0.035 | -0.021 | | MicroEnsemble | 2 | MicroDuet | 2 | 0.053 | 0.098 | -0.001 | 0.047 | -0.001 | 0.018 | -0.022 | 0.034 | 0.1 | 0.064 | 0.027 | 0.09 | | MicroEnsemble | 2 | StillLearning | 3 | 0.049 | 0.096 | -0.004 | 0.035 | -0.005 | 0.008 | -0.022 | 0.018 | 0.086 | 0.086 | 0.023 | 0.103 | | MicroEnsemble | 2 | Huracan | 8 | 0.009 | 0.024 | -0.021 | 0.037 | -0.015 | -0.013 | -0.036 | 0.022 | 0.047 | -0.027 | -0.016 | 0.003 | | LP | 3 | LPM | 5 | 0.027 | 0.052 | -0.004 | 0.008 | -0.001 | -0.001 | -0.043 | 0.041 | 0.044 | 0.023 | 0.021 | 0.003 | | AIFS | 4 | AIFSgaia | 6 | 0.027 | 0.048 | 0.02 | -0.016 | -0.008 | -0.001 | -0.026 | 0.039 | 0.073 | 0.012 | 0.026 | -0.035 | | AIFS | 4 | AIFShera | 9 | 0.004 | 0.01 | 0.005 | -0.031 | -0.004 | -0.008 | -0.029 | -0.002 | 0.056 | -0.009 | 0.01 | -0.033 | | AIFS | 4 | AIFSthalassa | 15 | -0.006 | -0.027 | 0.011 | -0.021 | -0.002 | 0.022 | -0.022 | 0.03 | 0.014 | -0.107 | 0.011 | -0.065 | | scienceAI | 5 | findforecast | 10 | 0.002 | 0.001 | -0.001 | 0.013 | -0.005 | 0.014 | -0.015 | 0.011 | 0.003 | -0.003 | -0.002 | 0.008 | | 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 | 6 | 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 | 6 | 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 | | KITKangu | 6 | KanguS2SEasyUQ | 35 | -1.351 | -1.471 | -1.254 | -1.257 | -1.008 | -1.337 | -1.196 | -1.421 | -1.412 | -1.552 | -1.146 | -1.458 | | CliMA | 7 | CliMAWeather2 | 16 | -0.214 | -0.242 | -0.288 | -0.188 | -0.113 | -0.047 | -0.166 | -0.178 | -0.225 | -0.35 | -0.274 | -0.226 | | CliMA | 7 | CliMAWeather | 18 | -0.238 | -0.286 | -0.225 | -0.234 | -0.238 | -0.137 | -0.217 | -0.14 | -0.252 | -0.387 | -0.297 | -0.26 | | FengWuW2S | 8 | FengWu2 | 17 | -0.236 | -0.257 | -0.367 | 0.007 | -0.149 | 0.047 | -0.128 | -0.211 | -0.128 | -0.332 | -0.45 | -0.26 | | FengWuW2S | 8 | FengWu | 20 | -0.287 | -0.393 | -0.273 | -0.081 | -0.11 | -0.133 | -0.108 | -0.228 | -0.392 | -0.517 | -0.343 | -0.299 | | NordicS2S | 9 | NordicS2S1 | 19 | -0.275 | -0.251 | -0.263 | -0.312 | -0.302 | -0.329 | -0.383 | -0.161 | -0.273 | -0.352 | -0.267 | -0.312 | | NordicS2S | 9 | NordicS2S3 | 25 | -0.498 | -0.643 | -0.302 | -0.31 | -0.395 | -0.526 | -0.463 | -0.306 | -0.605 | -0.699 | -0.384 | -0.683 | | NordicS2S | 9 | NordicS2S2 | 28 | -0.575 | -0.603 | -0.587 | -0.592 | -0.498 | -0.563 | -0.677 | -0.482 | -0.707 | -0.677 | -0.526 | -0.537 | | HAPPY | 10 | AZN | 21 | -0.34 | -0.419 | -0.278 | -0.878 | -0.31 | -0.023 | -0.25 | -0.292 | -0.598 | -0.3 | -0.31 | -0.798 |
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Figures showing aggregated RPSSs for best-performing model from top 10 teams
