This page provides an overview of regional forecast skill for the DJF 2025 period. Forecast scores are updated automatically every week throughout the competitive period. The current data includes 10 forecasts initialized between Thursday 13th November 2025 and Thursday 15th January 2026 (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:

DJF 2025 Regional RPSSs (Excel format)

Regional scores for the top 10 teams of global, period-aggregated, variable-averaged RPSSs

Team nameTeam rankModel nameModel rankGlobalTropicsNHem. ExTro.SHem. ExTro.NHem. PolarSHem. PolarEuropeN. Amer.S. Amer.AfricaAsiaOceania
MicroEnsemble1MicroDuet10.0730.0960.0750.0290.0490.030.0320.1130.0080.10.0710.039
MicroEnsemble1StillLearning20.0610.0750.0770.0250.0530.0120.030.111-0.0060.0970.0630.03
MicroEnsemble1Huracan50.0440.0390.069-0.0020.0640.0180.0350.124-0.0460.0150.0370.019
LP2LPM30.0560.0710.072-0.0040.0690.0040.0350.127-0.0340.0750.0450.032
AIFS3AIFShera40.0530.0450.088-0.0080.0880.0160.090.111-0.0570.0440.070.022
AIFS3AIFSgaia70.0340.0320.0690.0040.037-0.0120.0120.121-0.0720.0220.0360.035
AIFS3AIFSthalassa80.0260.0080.0570.0140.0730.0010.0230.11-0.089-0.0090.0310.037
CMAandFDU4FengshunHybrid60.0430.0510.062-0.0010.063-0.0080.0340.087-0.020.0510.0530.015
CMAandFDU4FengshunAdjust90.0220.0280.0310.00.033-0.0090.0160.017-0.0180.0460.033-0.0
CMAandFDU4Fengshun15-0.008-0.0110.008-0.009-0.01-0.018-0.023-0.035-0.0630.0010.03-0.053
UWAtmosNVIDIA5DLESyMS2Sv1100.0130.0130.0190.0130.0240.0110.0230.031-0.021-0.0-0.0030.015
CliMA6CliMAWeather2110.0110.029-0.001-0.0120.002-0.008-0.014-0.009-0.0060.0410.008-0.018
CliMA6CliMAWeather16-0.024-0.02-0.027-0.045-0.025-0.02-0.039-0.033-0.068-0.01-0.023-0.044
scienceAI7zephyr120.003-0.0160.0170.0180.0210.043-0.0160.04-0.045-0.0750.004-0.006
scienceAI7findforecast13-0.006-0.0-0.0080.008-0.027-0.017-0.033-0.009-0.023-0.001-0.004-0.005
scienceAI7ngcm14-0.007-0.0160.0160.0170.009-0.042-0.0070.031-0.048-0.0670.0030.002
FengWuW2S8FengWu217-0.03-0.06-0.0030.0160.006-0.029-0.036-0.061-0.085-0.031-0.005-0.044
FengWuW2S8FengWu18-0.032-0.055-0.0110.002-0.011-0.016-0.041-0.057-0.099-0.025-0.021-0.026
FengWuW2S8FengWu319-0.039-0.077-0.007-0.019-0.0180.016-0.016-0.049-0.096-0.055-0.018-0.066
JR9slowMamba20-0.051-0.077-0.009-0.0280.007-0.123-0.0220.007-0.121-0.076-0.024-0.089
NordicS2S10NordicS2S121-0.117-0.125-0.078-0.075-0.257-0.059-0.073-0.088-0.077-0.125-0.183-0.132
NordicS2S10NordicS2S222-0.119-0.169-0.0540.001-0.17-0.094-0.145-0.086-0.137-0.176-0.094-0.146
NordicS2S10NordicS2S323-0.142-0.15-0.096-0.114-0.219-0.196-0.194-0.146-0.162-0.181-0.085-0.109
Team nameTeam rankModel nameModel rankGlobalTropicsNHem. ExTro.SHem. ExTro.NHem. PolarSHem. PolarEuropeN. Amer.S. Amer.AfricaAsiaOceania
MicroEnsemble1MicroDuet10.0460.0620.0540.0010.0330.0040.0210.0610.0130.0870.0510.002
MicroEnsemble1StillLearning20.0350.0460.0530.00.035-0.0250.0150.058-0.0020.0890.043-0.009
MicroEnsemble1Huracan60.012-0.0060.048-0.0250.045-0.0110.0380.058-0.045-0.0130.017-0.022
LP2LPM30.020.0190.053-0.0230.048-0.0570.0390.053-0.0130.0560.027-0.029
CMAandFDU3FengshunHybrid40.0140.0050.041-0.0150.045-0.0150.0340.04-0.0380.0330.023-0.026
CMAandFDU3FengshunAdjust100.001-0.0110.025-0.010.032-0.0150.010.025-0.0270.0220.002-0.044
CMAandFDU3Fengshun15-0.024-0.03-0.017-0.0280.01-0.018-0.054-0.055-0.05-0.0350.019-0.077
AIFS4AIFShera50.012-0.0010.053-0.0170.043-0.0180.0760.03-0.0390.0270.04-0.044
AIFS4AIFSgaia80.006-0.0060.043-0.0140.019-0.0210.0260.06-0.0420.0070.008-0.034
AIFS4AIFSthalassa17-0.04-0.1010.028-0.0270.038-0.0380.0480.03-0.193-0.084-0.01-0.062
CliMA5CliMAWeather270.0120.0180.012-0.0120.0050.0110.0030.0-0.0090.0250.015-0.013
CliMA5CliMAWeather14-0.021-0.026-0.016-0.04-0.0240.011-0.015-0.034-0.052-0.011-0.015-0.045
scienceAI6findforecast90.0040.0060.012-0.0-0.013-0.015-0.0420.016-0.0090.0050.009-0.01
scienceAI6zephyr11-0.004-0.0130.0040.0050.0060.015-0.0170.014-0.009-0.046-0.008-0.032
scienceAI6ngcm13-0.014-0.0290.0060.0020.01-0.023-0.0130.009-0.032-0.036-0.01-0.033
UWAtmosNVIDIA7DLESyMS2Sv112-0.008-0.0160.018-0.0170.002-0.0350.045-0.014-0.028-0.002-0.001-0.034
FengWuW2S8FengWu316-0.04-0.045-0.026-0.024-0.032-0.075-0.046-0.006-0.086-0.029-0.053-0.048
FengWuW2S8FengWu19-0.052-0.098-0.005-0.026-0.012-0.018-0.035-0.04-0.12-0.054-0.04-0.098
FengWuW2S8FengWu220-0.053-0.091-0.015-0.0220.005-0.047-0.044-0.054-0.109-0.045-0.041-0.073
NordicS2S9NordicS2S218-0.041-0.050.016-0.052-0.035-0.136-0.0410.004-0.038-0.031-0.025-0.103
NordicS2S9NordicS2S122-0.085-0.119-0.055-0.025-0.073-0.068-0.062-0.062-0.068-0.132-0.101-0.133
NordicS2S9NordicS2S323-0.134-0.14-0.126-0.043-0.087-0.214-0.19-0.038-0.117-0.213-0.184-0.073
JR10slowMamba21-0.075-0.101-0.06-0.036-0.008-0.095-0.017-0.071-0.105-0.107-0.057-0.075

Figures showing aggregated RPSSs for best-performing model from top 10 teams

Figures showing evolution of skill scores

Figures showing percentage of grid points with positive period-aggregated RPSSs

Figures showing observed conditions with respect to defined ERA5 climatology

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