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 5 forecasts initialized between Thursday 14th August 2025 and Thursday 11th 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 | 0.142 | 0.021 | 0.005 | -0.016 | -0.026 | 0.008 | 0.034 | 0.146 | 0.107 | 0.057 | 0.088 | | CMAandFDU | 1 | FengshunHybrid | 3 | 0.07 | 0.126 | 0.026 | 0.01 | 0.009 | -0.005 | -0.005 | 0.05 | 0.097 | 0.106 | 0.066 | 0.061 | | CMAandFDU | 1 | Fengshun | 10 | 0.017 | 0.041 | 0.006 | -0.023 | 0.003 | -0.049 | 0.01 | 0.013 | 0.016 | -0.006 | 0.048 | -0.016 | | MicroEnsemble | 2 | MicroDuet | 2 | 0.072 | 0.116 | 0.033 | 0.047 | 0.019 | 0.012 | 0.022 | 0.032 | 0.087 | 0.075 | 0.07 | 0.113 | | MicroEnsemble | 2 | StillLearning | 4 | 0.067 | 0.109 | 0.029 | 0.043 | 0.012 | 0.006 | 0.025 | 0.024 | 0.078 | 0.101 | 0.059 | 0.116 | | MicroEnsemble | 2 | Huracan | 9 | 0.027 | 0.032 | 0.021 | 0.028 | 0.004 | -0.001 | 0.015 | 0.024 | 0.012 | -0.025 | 0.038 | 0.03 | | AIFS | 3 | AIFSgaia | 5 | 0.051 | 0.084 | 0.031 | 0.001 | 0.015 | -0.02 | 0.013 | 0.01 | 0.071 | 0.063 | 0.067 | -0.012 | | AIFS | 3 | AIFShera | 6 | 0.05 | 0.068 | 0.04 | 0.012 | 0.021 | 0.007 | 0.014 | 0.013 | 0.089 | 0.039 | 0.075 | 0.021 | | AIFS | 3 | AIFSthalassa | 8 | 0.04 | 0.061 | 0.026 | -0.01 | 0.021 | -0.006 | 0.005 | 0.006 | 0.051 | 0.037 | 0.062 | -0.013 | | LP | 4 | LPM | 7 | 0.05 | 0.073 | 0.031 | 0.046 | 0.014 | -0.009 | -0.004 | 0.038 | 0.062 | 0.035 | 0.057 | 0.051 | | scienceAI | 5 | findforecast | 11 | 0.003 | 0.002 | 0.003 | 0.014 | -0.012 | 0.014 | -0.02 | 0.018 | 0.006 | -0.003 | -0.005 | 0.012 | | 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 | -1.041 | -1.12 | -0.975 | -1.076 | -0.862 | -0.956 | -0.919 | -0.976 | -1.141 | -1.066 | -0.97 | -1.369 | | CliMA | 7 | CliMAWeather2 | 16 | -0.15 | -0.156 | -0.212 | -0.15 | -0.112 | -0.007 | -0.123 | -0.182 | -0.149 | -0.214 | -0.181 | -0.136 | | CliMA | 7 | CliMAWeather | 19 | -0.215 | -0.258 | -0.205 | -0.324 | -0.201 | -0.082 | -0.192 | -0.188 | -0.243 | -0.287 | -0.246 | -0.324 | | WindBorne | 8 | WeatherMesh | 17 | -0.174 | -0.129 | -0.187 | -0.315 | -0.174 | -0.396 | -0.192 | -0.208 | -0.124 | -0.169 | -0.166 | -0.229 | | FengWuW2S | 9 | FengWu2 | 18 | -0.21 | -0.189 | -0.337 | -0.076 | -0.208 | -0.053 | -0.252 | -0.108 | -0.083 | -0.305 | -0.426 | -0.245 | | FengWuW2S | 9 | FengWu | 21 | -0.246 | -0.328 | -0.228 | -0.134 | -0.107 | -0.163 | -0.165 | -0.161 | -0.324 | -0.424 | -0.32 | -0.274 | | NordicS2S | 10 | NordicS2S1 | 20 | -0.237 | -0.155 | -0.255 | -0.314 | -0.274 | -0.449 | -0.279 | -0.173 | -0.214 | -0.21 | -0.183 | -0.249 | | NordicS2S | 10 | NordicS2S3 | 23 | -0.364 | -0.376 | -0.359 | -0.335 | -0.329 | -0.519 | -0.292 | -0.316 | -0.433 | -0.454 | -0.291 | -0.414 | | NordicS2S | 10 | NordicS2S2 | 26 | -0.517 | -0.547 | -0.494 | -0.448 | -0.46 | -0.555 | -0.411 | -0.535 | -0.645 | -0.491 | -0.436 | -0.618 |
|
| 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 | 0.054 | 0.102 | 0.015 | 0.02 | -0.012 | 0.015 | 0.002 | 0.054 | 0.085 | 0.087 | 0.018 | 0.06 | | CMAandFDU | 1 | FengshunHybrid | 4 | 0.037 | 0.069 | 0.011 | 0.027 | -0.022 | 0.028 | -0.004 | 0.052 | 0.053 | 0.055 | 0.004 | 0.001 | | CMAandFDU | 1 | Fengshun | 9 | 0.001 | 0.007 | 0.013 | 0.019 | -0.004 | -0.032 | 0.013 | 0.024 | 0.012 | -0.026 | 0.007 | -0.06 | | MicroEnsemble | 2 | MicroDuet | 2 | 0.052 | 0.091 | 0.002 | 0.054 | 0.008 | 0.032 | -0.016 | 0.045 | 0.084 | 0.062 | 0.027 | 0.076 | | MicroEnsemble | 2 | StillLearning | 3 | 0.049 | 0.089 | -0.001 | 0.043 | 0.003 | 0.024 | -0.019 | 0.035 | 0.07 | 0.082 | 0.023 | 0.097 | | MicroEnsemble | 2 | Huracan | 7 | 0.01 | 0.019 | -0.016 | 0.048 | 0.0 | 0.004 | -0.022 | 0.039 | 0.028 | -0.031 | -0.011 | 0.002 | | LP | 3 | LPM | 5 | 0.025 | 0.043 | -0.004 | 0.02 | 0.011 | 0.017 | -0.04 | 0.052 | 0.038 | 0.024 | 0.012 | -0.014 | | AIFS | 4 | AIFSgaia | 6 | 0.017 | 0.031 | 0.014 | -0.008 | -0.008 | 0.005 | -0.008 | 0.056 | 0.057 | -0.002 | -0.006 | -0.048 | | AIFS | 4 | AIFShera | 8 | 0.003 | 0.006 | -0.001 | -0.007 | -0.014 | 0.007 | -0.017 | 0.021 | 0.055 | -0.017 | -0.017 | -0.018 | | AIFS | 4 | AIFSthalassa | 15 | -0.011 | -0.037 | 0.003 | -0.014 | 0.002 | 0.036 | -0.01 | 0.038 | 0.011 | -0.123 | -0.017 | -0.046 | | scienceAI | 5 | findforecast | 10 | 0.001 | -0.0 | -0.003 | 0.022 | -0.007 | 0.012 | -0.004 | 0.024 | -0.005 | -0.008 | -0.013 | 0.028 | | 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 | 34 | -1.076 | -1.179 | -0.995 | -0.993 | -0.792 | -1.065 | -0.952 | -1.141 | -1.144 | -1.266 | -0.9 | -1.119 | | CliMA | 7 | CliMAWeather2 | 16 | -0.213 | -0.245 | -0.296 | -0.159 | -0.114 | -0.016 | -0.228 | -0.197 | -0.235 | -0.334 | -0.267 | -0.22 | | CliMA | 7 | CliMAWeather | 18 | -0.277 | -0.324 | -0.271 | -0.267 | -0.281 | -0.164 | -0.232 | -0.117 | -0.3 | -0.431 | -0.371 | -0.326 | | FengWuW2S | 8 | FengWu2 | 17 | -0.23 | -0.288 | -0.306 | 0.012 | -0.111 | 0.055 | -0.111 | -0.223 | -0.127 | -0.334 | -0.389 | -0.302 | | FengWuW2S | 8 | FengWu | 19 | -0.281 | -0.41 | -0.227 | -0.07 | -0.076 | -0.155 | -0.1 | -0.211 | -0.395 | -0.503 | -0.3 | -0.346 | | NordicS2S | 9 | NordicS2S1 | 20 | -0.288 | -0.248 | -0.288 | -0.304 | -0.387 | -0.326 | -0.392 | -0.193 | -0.25 | -0.319 | -0.326 | -0.322 | | NordicS2S | 9 | NordicS2S3 | 25 | -0.525 | -0.659 | -0.347 | -0.276 | -0.464 | -0.554 | -0.475 | -0.293 | -0.575 | -0.715 | -0.471 | -0.679 | | NordicS2S | 9 | NordicS2S2 | 27 | -0.589 | -0.608 | -0.615 | -0.63 | -0.553 | -0.575 | -0.702 | -0.501 | -0.732 | -0.66 | -0.568 | -0.534 | | HAPPY | 10 | AZN | 21 | -0.299 | -0.353 | -0.252 | -0.678 | -0.335 | -0.0 | -0.21 | -0.311 | -0.489 | -0.256 | -0.28 | -0.609 |
|
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
