Relative to individual extreme weather and climate events，Compound Wind and Precipitation Extremes（CWPE），which result from extreme winds and extreme precipitation，have a profound impact on the economy and daily life in coastal areas. In this study，we utilized the simulations from 17 models within the Coupled Model Intercomparison Project Phase 6（CMIP6）for the period 1961-2000 of CWPE in the eastern coastal region of China as a training set，and established a Deep Learning（DL）model employing a multi-layer perceptron neural network. By constructing a loss function suitable for CWPE and optimizing the model accordingly， we have developed a DL model aimed at reducing the simulation bias and uncertainty of CMIP6 models for CWPE. The research results indicate that the majority of CMIP6 models possess a relatively good simulation capability for CWPE in the eastern coastal region of China，with the Multi-Model Ensemble Mean（MME-Mean） and the Multi-Model Ensemble Median（MME-Median）demonstrating better performance in assessments com‐ pared to individual models. The DL model constructed with the Mean Squared Error（MSE）function as the loss function performs worse in terms of Taylor Skill Score（TS）and Root Mean Squared Error（RMSE）compared to the statistical results of the Multi-Model Ensemble. Incorporating the Ratio of the Standard Deviation（RSD） from climate evaluation metrics and an underestimation constraint function into the MSE loss function can significantly enhance the performance of the DL model in terms of TS and RMSE. Therefore，the DL model trained with a weighted loss function constructed from MSE，RSD，and underestimation constraint function is defined as DL-MRM，while the DL model trained solely with MSE as the loss function is defined as DL-MSE. By com‐ paring and analyzing the performance of the two DL models in simulating CWPE over the eastern coastal region of China from 2001 to 2014，as well as the performance of DL-MRM relative to multi-model ensemble methods， we conclude：（1）Both DL models exhibit underestimation in their simulation results，but the bias of the DLMRM is lower than that of the DL-MSE，being closer to the observations. Specifically，in the study area，the relative bias of the DL-MRM is lower than that of the DL-MSE by about 63%，and the average relative bias is reduced by approximately 20%.（2）The DL-MRM has a lower overall bias compared to the MME-Mean and MME-Median，with simulation results that are closer to the observations. In the study area，the DL-MRM has a lower relative bias in 67% and 62% of the area compared to the MME-Mean and MME-Median，respectively， and the average relative bias is reduced by approximately 10% and 20%，respectively. Overall，by integrating the RSD and underestimation constraint functions to construct a weighted loss function for model optimization，a DL model suitable for improving the simulation of CWPE by CMIP6 models was established. This indicates that the combination of deep learning methods can more effectively reduce the biases in CMIP6 model simulations of CWPE compared to traditional multi-model ensemble methods.