Assessing predictive attribution in NMME forecasts of summer precipitation in eastern china using deep learning
In one sentence. A Res34-Unet post-processing of NMME six-month forecasts of eastern China summer rainfall identifies WPSH and SST as key drivers and shows which models capture them.
Abstract
Due to systematic errors in models and the special geographic location of eastern China, most global climate models exhibit significant biases in predicting summer precipitation in this region. This study evaluates the North American Multi-Model Ensemble (NMME) forecasts for eastern China, with a lead time of six months.While NMME simulates precipitation climatology well, it poorly predicts anomalies. Using the Res34-Unet deep learning post-processing method, which has been proven to enhance NMME’s forecasts, we explore that Western Pacific Subtropical High (WPSH) and sea surface temperature (SST) are critical in enhancing forecast accuracy. Among the four models evaluated, only GEM-NEMO (correlation of 0.538 with the WPSH) and CanSIPS-IC3 (which partly captured the impact of SST anomalies on precipitation) partially reflected the key factors identified by deep learning. Simulating these factors more accurately could greatly enhance NMME’s predictive skill for summer precipitation.
Labels
- Topic
- Post-processing & calibration, Sub-seasonal to seasonal
- Type
- Evaluation or intercomparison of existing models
- Method
- CNN / U-Net
- Time scale
- Seasonal to annual
- Domain
- Regional
- Data
- NWP analysis or forecasts (IFS, GFS, ...)
- Status
- Research
- Identifiers
- DOI:10.1038/s41612-024-00835-7 OpenAlex:W4405596923
- Added
- 2026-10-22
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