Two deep learning-based bias-correction pathways improve summer precipitation prediction over China

As most global climate models (GCM) suffer from large biases in simulating/predicting summer precipitation over China, it is of great importance to develop suitable bias-correction methods. This study proposes two pathways of bias-correction with deep learning (DL) models incorporated. One is the deterministic pathway (DP), in which the bias correction is directly applied to the precipitation forecasts. The other one, namely the probability pathway (PP), corrects the forecasted precipitation anomalies using a conditional probability method before being added to the observational climatology. These two pathways have been applied to correct the precipitation forecasts based on a GCM prediction system Nanjing University of Information Science and Technology Climate Forecast System version 1.0 (NUIST-CFS1.0). The applications of DL models in the both pathways yield higher resolution of corrected predictions than the uncorrected ones. Both pathways improve summer precipitation predictions at 4-month lead. Moreover, the DP correction shows a better performance in predicting extreme precipitation, while the PP is proficient in correcting the spatial pattern of precipitation anomalies over China. The present results highlight the importance of the application of appropriate correction strategy for different prediction purposes.

Automated summary

As most global climate models have large biases in simulating summer precipitation over China, it is crucial to develop suitable bias-correction methods. This study proposes two pathways of bias-correction with deep learning models incorporated, namely the deterministic pathway (DP) and the probability pathway (PP). The applications of deep learning models in both pathways improve the resolution of corrected predictions compared to the uncorrected ones and enhance summer precipitation predictions at a 4-month lead. The DP correction performs better in predicting extreme precipitation, while the PP is proficient in correcting the spatial pattern of precipitation anomalies over China.