Tien et al. (2026) Evaluation of Seasonal Precipitation Forecasts over the Upper Tigris–Euphrates Basin

⚠️ Warning: This summary was generated from the abstract only, as the full text was not available.

Identification

• Journal: Journal of Hydrometeorology
• Year: 2026
• Date: 2026-04-01
• Authors: Yu-Chuan Tien, Mekonnen Gebremichael, Amin Dezfuli
• DOI: 10.1175/jhm-d-24-0112.1

Research Groups

Not available from the provided abstract.

Short Summary

This study assesses the performance of seasonal precipitation forecasts for the upper Tigris–Euphrates basin, revealing that statistical models based on atmospheric–oceanic indices, particularly a hybrid model, outperform complex dynamic models from the North American Multi-Model Ensemble.

Objective

• To assess the performance of seasonal precipitation forecasts for the wet season (December–April) in the upper Tigris–Euphrates (T-E) basin, using both dynamic models from the North American Multi-Model Ensemble (NMME) and newly developed statistical models based on atmospheric–oceanic indices.

Study Configuration

• Spatial Scale: Upper Tigris–Euphrates (T-E) basin.
• Temporal Scale: Wet season (December–April), seasonal forecasts.

Methodology and Data

• Models used:
  • Dynamic models from the North American Multi-Model Ensemble (NMME), including CanCM4i–initialization case 3 (CanCM4i-IC3), CFSv2, and GFDL-Seamless System for Prediction and Earth System Research (GFDL-SPEAR).
  • Newly developed statistical models based on atmospheric–oceanic indices.
  • Simple linear regression model based on atmospheric–oceanic indices.
  • Hybrid statistical model incorporating CanCM4i-IC3 outputs and atmospheric–oceanic indices.
• Data sources:
  • Integrated Multi-satellitE Retrievals for Global Precipitation Measurement (GPM) (IMERG) product (satellite-based) as the reference dataset.
  • Atmospheric–oceanic indices: North Atlantic Oscillation (NAO), El Niño–Southern Oscillation (ENSO), and dipole mode index (DMI).

Main Results

• The correlation between NMME forecasts and IMERG observations ranges from 0.44 to 0.70, with CanCM4i-IC3 exhibiting the highest correlation.
• All NMME models show some bias, with varying tendencies to either underestimate or overestimate precipitation.
• CFSv2 and CanCM4i-IC3 demonstrate skill in predicting dry conditions, while CFSv2 and GFDL-SPEAR show skill in forecasting wet conditions.
• All models tend to underestimate extreme precipitation events.
• Atmospheric–oceanic indices (NAO, ENSO, DMI) significantly influence wet-season precipitation variability, with NAO showing the strongest association.
• A simple linear regression model based on atmospheric–oceanic indices outperforms NMME models.
• A hybrid statistical model, incorporating CanCM4i-IC3 outputs along with atmospheric–oceanic indices, outperforms both stand-alone statistical and NMME models.

Contributions

• Provides a comprehensive evaluation of seasonal precipitation forecast performance for the upper Tigris–Euphrates basin using both dynamic and statistical approaches.
• Highlights the significant influence of key atmospheric–oceanic indices (NAO, ENSO, DMI) on wet-season precipitation variability in the region.
• Demonstrates that statistical models, particularly a hybrid approach combining dynamic model outputs with atmospheric–oceanic indices, can outperform complex dynamic models for seasonal precipitation forecasting in this region.
• Proposes a computationally efficient and reliable alternative for seasonal precipitation forecasting in the upper Tigris–Euphrates basin.

Funding

Not available from the provided abstract.

Citation

@article{Tien2026Evaluation,
  author = {Tien, Yu-Chuan and Gebremichael, Mekonnen and Dezfuli, Amin},
  title = {Evaluation of Seasonal Precipitation Forecasts over the Upper Tigris–Euphrates Basin},
  journal = {Journal of Hydrometeorology},
  year = {2026},
  doi = {10.1175/jhm-d-24-0112.1},
  url = {https://doi.org/10.1175/jhm-d-24-0112.1}
}