## 1 Introduction
This approach harnesses the strengths of different models while mitigating their limitations, thus enhancing the precision and predictability of hydrological forecasts [31; 32; 33]. Additionally, systematic bias and insufficient dispersion in meteorological ensemble forecasts, coupled with low spatial and temporal resolution when integrated with hydrological models and flawed spatio-temporal correlation structures between variables, necessitate enhancements through statistical post-processing techniques [34; 35]. Over the past two decades, a variety of statistical post-processing methods have been developed, including linear bias correction [36], rank histogram techniques [37], reliability diagram techniques [38], Bayesian model averaging [39], kernel density estimation [40], logistic regression [41], non-homogeneous Gaussian regression [42], and so on. Research has demonstrated that applying these post-processing techniques to raw meteorological ensemble forecasts significantly improves the accuracy and reliability of hydrological predictions [43; 44; 45]. In light of these advances, this study hypothesizes that integrating statistical post-processing methods with GE and multiple hydrological models (MHM) will significantly enhance flood forecasting accuracy and reliability. To test this hypothesis, we propose the following research objectives. (1) Evaluate the effectiveness of the statistical post-processing method: assess how post-processing improves EPF performance in flood forecasting by correcting biases and addressing insufficient dispersion. (2) Investigate the utility of a GE: determine if a GE, which aggregates EPFs from multiple numerical weather prediction centers, provides more accurate flood peak predictions than a single EPF. (3) Assess the potential of MHM: evaluate whether combining different hydrological models enhances-flood forecasting reliability compared to a single hydrological model (SHM), particularly in capturing event variability and uncertainty. (4) Examine the benefits of integrating GE with MHM: explore whether the combined use of GE and MHM improves the overall quality of flood forecasting, leveraging the strengths of both approaches for more precise and dependable predictions. The primary objective of this research is to identify the most effective combination of EPF and hydrological models (EPF+HM) for improving flood forecasting. The novelty of this study lies in developing and implementing a comprehensive framework that integrates GE and MHM for ensemble flood forecasting (EFF). By applying this innovative framework to the Qingjiang River Basin in the mountainous central-western region of China--characterized by high population density and vulnerability to frequent rainstorms and floods--we aim to address challenges such as systematic biases, insufficient dispersion, and spatio-temporal discrepancies in traditional models. This research represents the first attempt to apply multi-model techniques for ensemble flood forecasting in this region. The outcomes are expected to contribute to developing more effective flood forecasts, particularly in regions with similar topographical and climatic challenges.