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 11th 18th September 2025 (inclusive). For a detailed description of the outputs, please refer to the section's overview.
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| title | Forecast window 1 (days 19 to 25) |
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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 |
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| CMAandFDUMicroEnsemble | 1 | FengshunAdjustMicroDuet | 1 | 0.072073 | 0.142116 | 0.021032 | 0.005053 | -0.016024- | 0.026021 | 0.008018 | 0.034044 | 0.146082 | 0.107072 | 0.057069 | 0.088117 | | CMAandFDUMicroEnsemble | 1 | FengshunHybridStillLearning | 3 | 0.07067 | 0.126108 | 0.026029 | 0.01048 | 0.009018 | -0.005017- | 0.005019 | 0.05039 | 0.097071 | 0.106097 | 0.066059 | 0.061125 | | CMAandFDUMicroEnsemble | 1 | FengshunHuracan | 109 | 0.017027 | 0.041032 | 0.006019 | -0.023035 | 0.003014 | -0.049008 | 0.01014 | 0.013038 | 0.016006 | -0.006036 | 0.048039 | -0.016043 | | MicroEnsembleCMAandFDU | 2 | MicroDuetFengshunAdjust | 2 | 0.07207 | 0.11614 | 0.033017 | 0.047016 | -0.019016 | -0.01202 | 0.022013 | 0.032038 | 0.087138 | 0.075105 | 0.07046 | 0.113087 | | MicroEnsembleCMAandFDU | 2 | StillLearningFengshunHybrid | 4 | 0.067065 | 0.109119 | 0.029019 | 0.04302 | 0.012008 | 0.006002 | -0.025002 | 0.02406 | 0.078096 | 0.101 | 0.059045 | 0.116044 | | MicroEnsembleCMAandFDU | 2 | HuracanFengshun | 910 | 0.027012 | 0.032031 | 0.021006 | -0.028008 | 0.004006 | -0.001053 | 0.015023 | 0.024023 | 0.012024 | -0.02501 | 0.038031 | -0.0305 | | AIFSLP | 3 | AIFSgaiaLPM | 5 | 0.051049 | 0.084074 | 0.031026 | 0.001042 | 0.015019 | -0.020 | -0.013012 | 0.01045 | 0.071058 | 0.063035 | 0.067054 | -0.012052 | | AIFS | 34 | AIFShera | 6 | 0.0504 | 0.06805 | 0.04031 | 0.012018 | 0.021024 | 0.007013 | 0.014019 | 0.013029 | 0.089071 | 0.039027 | 0.075048 | 0.021019 | | AIFS | 34 | AIFSthalassaAIFSgaia | 87 | 0.04037 | 0.061065 | 0.026017 | -0.01008 | 0.021006 | -0.006014 | 0.005007 | 0.006025 | 0.051052 | 0.037041 | 0.062036 | -0.013012 | | LPAIFS | 4 | LPMAIFSthalassa | 78 | 0.05036 | 0.073058 | 0.031018 | 0.046004 | 0.014022 | -0.009002 | -0.0040 | 0.038022 | 0.062049 | 0.03502 | 0.05704 | 0.051012 | | scienceAI | 5 | findforecast | 11 | 0.003001 | -0.002001 | -0.003 | 0.014019 | -0.012009 | 0.014007 | -0.02015 | 0.018028 | -0.006 | -0.003014 | -0.005014 | 0.012031 | | 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 | -1.041063 | -1.12154 | -0.975984 | -1.076074 | -0.862847 | -01.956001 | -0.919952 | -1.0.976 | -1.141177 | -1.066145 | -0.97957 | -1.369321 | | CliMA | 7 | CliMAWeather2 | 16 | -0.1513 | -0.156143 | -0.212178 | -0.15114 | -0.112084 | -0.007003 | -0.123112 | -0.182133 | -0.149147 | -0.214203 | -0.181156 | -0.136104 | | CliMA | 7 | CliMAWeather | 1922 | -0.215259 | -0.258303 | -0.205256 | -0.324327 | -0.201232 | -0.082119 | -0.192224 | -0.188166 | -0.243287 | -0.28734 | -0.246332 | -0.32437 | | WindBorne | 8 | WeatherMesh | 17 | -0.174166 | -0.129125 | -0.187163 | -0.315296 | -0.17414 | -0.396424 | -0.1922 | -0.208 | -0.124151 | -0.169163 | -0.166123 | -0.229226 | | FengWuW2S | 9 | FengWu2 | 18 | -0.21191 | -0.189198 | -0.337289 | -0.076043 | -0.208158 | -0.053026 | -0.252219 | -0.108117 | -0.083 | -0.305315 | -0.426358 | -0.245224 | | FengWuW2S | 9 | FengWu | 2119 | -0.246236 | -0.328332 | -0.228194 | -0.134111 | -0.107074 | -0.163172 | -0.165146 | -0.161139 | -0.324339 | -0.424427 | -0.32276 | -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 | | 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 | 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 |
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| title | Forecast window 2 (days 26 to 32) |
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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 |
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| CMAandFDUMicroEnsemble | 1 | FengshunAdjustMicroDuet | 1 | 0.054046 | 0.102081 | -0.015001 | 0.02063 | -0.012009 | 0.015034 | -0.002031 | 0.054052 | 0.085077 | 0.08706 | 0.018022 | 0.06086 | | CMAandFDUMicroEnsemble | 1 | FengshunHybridStillLearning | 42 | 0.037043 | 0.069078 | -0.011002 | 0.027052 | -0.022005 | 0.028033 | -0.004031 | 0.052044 | 0.053066 | 0.055075 | 0.004019 | 0.001089 | | CMAandFDUMicroEnsemble | 1 | Huracan | Fengshun79 | 0.009 | 0.001016 | -0.007019 | 0.013054 | 0.019003 | -0.004018 | -0.032043 | 0.013051 | 0.024 | -0.012034 | -0.026016 | 0.007 | -0.06 | 031 | | CMAandFDU | MicroEnsemble | 2 | MicroDuetFengshunAdjust | 23 | 0.052033 | 0.091071 | 0.002004 | 0.05401 | -0.008009 | 0.032004 | -0.016003 | 0.045036 | 0.08406 | 0.062084 | 0.027011 | 0.076038 | | MicroEnsembleCMAandFDU | 2 | StillLearningFengshunHybrid | 34 | 0.049029 | 0.089059 | -0.001002 | 0.043024 | -0.003016 | 0.024018 | -0.01901 | 0.035048 | 0.07045 | 0.082058 | -0.023001 | 0.097023 | | MicroEnsembleCMAandFDU | 2 | HuracanFengshun | 713 | -0.01006 | 0.019002 | -0.016003 | 0.0480 | -0.0005 | -0.004047- | 0.022009 | 0.039024 | 0.028001 | -0.031015 | -0.011002 | -0.002046 | | LP | 3 | LPM | 5 | 0.025022 | 0.043037 | -0.004 | 0.02026 | 0.011012 | 0.017023 | -0.04 | 0.052056 | 0.038039 | 0.024021 | 0.012008 | -0.014013 | | AIFS | 4 | AIFSgaia | 6 | 0.017013 | 0.031032 | -0.014003 | -0.008012 | -0.008005 | 0.005008 | -0.008018 | 0.056051 | 0.057054 | -0.002001 | -0.006015 | -0.048008 | | AIFS | 4 | AIFShera | 8 | 0.003004 | 0.006013 | -0.001009 | -0.007015 | -0.014006 | 0.00701 | -0.017016 | 0.021024 | 0.055054 | -0.017011 | -0.017023- | 0.018007 | | AIFS | 4 | AIFSthalassa | 15 | -0.011016 | -0.037035 | -0.003008 | -0.01401 | 0.002 | 0.036023 | -0.01021 | 0.038037 | 0.011006 | -0.123113 | -0.017023 | -0.046013 | | scienceAIKITKangu | 5 | findforecast | 10 | KanguPlusPlus | 9 | 0.0 | -0.0010 | -0.0 | -0.0030 | 0.0220 | -0.0070 | 0.0120 | -0.0040 | -0.0240 | -0.0050- | 0.0080 | -0.0130.028 | | scienceAIKITKangu | 5 | zephyrKanguParametricPrediction | 119 | 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 | ngcmzephyr | 119 | 0.0 | -0.0 | -0.0 | -0.0 | 0.0 | 0.0 | 0.0 | 0.0 | -0.0 | -0.0 | 0.0 | -0.0 | | KITKanguscienceAI | 65 | KanguPlusPlusngcm | 119 | 0.0 | -0.0 | -0.0 | -0.0 | 0.0 | 0.0 | 0.0 | 0.0 | -0.0 | -0.0 | 0.0 | -0.0 | | KITKanguscienceAI | 65 | KanguParametricPredictionfindforecast | 1114 | -0.001 | -0.0018 | -0.0009- | 0.0028 | 0.0004 | 0.0 | -0.001 | 0.0027 | -0.0024 | -0.0015 | -0.0025- | 0.0019 | | KITKangu | 65 | KanguS2SEasyUQ | 34 | -1.076086 | -1.179202 | -01.995004 | -01.993025 | -0.79284 | -1.065003 | -01.952014 | -1.141106 | -1.144168 | -1.266326 | -0.9955 | -1.119132 | | CliMA | 7 | CliMAWeather2 | 16 | -0.213186 | -0.245227 | -0.296245 | -0.159145 | -0.114086 | -0.016013 | -0.228206 | -0.197152 | -0.23522 | -0.334304 | -0.267226 | -0.22187 | | CliMA | 7 | CliMAWeather | 1821 | -0.27733 | -0.324389 | -0.271333 | -0.267368 | -0.281286 | -0.1642 | -0.232318 | -0.117109 | -0.3355 | -0.43149 | -0.371435 | -0.326416 | | FengWuW2S | 8 | FengWu2 | 17 | -0.23199 | -0.288261 | -0.306265 | 0.012029 | -0.111105 | 0.05509 | -0.111108 | -0.223226 | -0.127117 | -0.33433 | -0.389331 | -0.302178 | | FengWuW2S | 8 | FengWu | 1918 | -0.281255 | -0.4138 | -0.227205 | -0.07065 | -0.076064 | -0.155136 | -0.1109 | -0.211203 | -0.39537 | -0.503491 | -0.3252 | -0.346269 | | NordicS2SHAPPY | 9 | NordicS2S1AZN | 2019 | -0.288291 | -0.248328 | -0.288268 | -0.304688 | -0.387338- | 0.326021 | -0.392214 | -0.193396 | -0.25468 | -0.319227 | -0.326254 | -0.322569 | | NordicS2S | 910 | NordicS2S3NordicS2S1 | 2520 | -0.525305 | -0.659248 | -0.347334 | -0.276343 | -0.464387 | -0.554335 | -0.47548 | -0.29324 | -0.575289 | -0.71528 | -0.471333 | -0.679306 | | NordicS2S | 910 | NordicS2S2NordicS2S3 | 2725 | -0.589492 | -0.608577 | -0.615385 | -0.63298 | -0.553451 | -0.57559 | -0.702534 | -0.501267 | -0.732522 | -0.66694 | -0.568483 | -0.534559 | | HAPPYNordicS2S | 10 | AZNNordicS2S2 | 2127 | -0.299591 | -0.353615 | -0.252609 | -0.678591 | -0.335513 | -0.0603 | -0.21727 | -0.311465 | -0.489717 | -0.256631 | -0.28573 | -0.609545 |
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Figures showing aggregated RPSSs for best-performing model from top 10 teams
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| title | Near-surface air temperature (tas), forecast window 1 (days 19 to 25) |
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| title | Near-surface air temperature (tas), forecast window 2 (days 26 to 32) |
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| title | Mean sea level pressure (mslp), forecast window 1 (days 19 to 25) |
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| title | Mean sea level pressure (mslp), forecast window 2 (days 26 to 32) |
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| title | Accumulated precipitation (pr), forecast window 1 (days 19 to 25) |
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| title | Accumulated precipitation (pr), forecast window 2 (days 26 to 32) |
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Figures showing percentage of grid points with positive period-aggregated RPSSs
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| title | Near-surface air temperature (tas), forecast window 1 (days 19 to 25) |
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| title | Near-surface air temperature (tas), forecast window 2 (days 26 to 32) |
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| title | Mean sea level pressure (mslp), forecast window 1 (days 19 to 25) |
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| title | Mean sea level pressure (mslp), forecast window 2 (days 26 to 32) |
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| title | Accumulated precipitation (pr), forecast window 1 (days 19 to 25) |
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| title | Accumulated precipitation (pr), forecast window 2 (days 26 to 32) |
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