## 1 Introduction  
Floods are the most frequent natural disasters worldwide, posing significant threats to human lives and socio-economic development [1]. Research by [PERSON], [PERSON] [2] indicates that intensifying hydrological cycles across global watersheds are causing large-scale flooding events, often exceeding the capacity of existing flood protection infrastructure, a trend likely exacerbated by climate change. Since the late 1980s, flood management strategies have shifted from reliance on structural measures to embracing non-structural approaches [3, 4]. Among these, flood forecasting and early warning systems are considered the most effective and cost-efficient methods for mitigating flood risks and minimizing their impacts [5, 6, 7]. These systems provide critical information regarding water flow, levels, and the timing of peak floods. Timely warnings enable authorities and affected populations to implement effective response measures, underscoring the need to improve forecast accuracy and extend lead times. Flood forecasting can be categorized into deterministic and probabilistic forecasts. Deterministic forecasts provide a specific prediction, representing one possible outcome from a range of possibilities. They typically do not consider inherent uncertainties and often have shorter lead times [8; 9; 10]. Despite being derived from optimal initial conditions, deterministic forecasts may underestimate or overestimate actual outcomes. To address these limitations, the integration of ensemble precipitation forecasts (EPFs) into flood forecasting has gained traction in hydrological and meteorological research [11; 12; 13]. EPFs are generated by running multiple simulations from numerical weather prediction (NWP) models with slightly varied initial conditions or different model physics. By incorporating EPFs from NWPs into hydrological models, ensemble hydrological forecasts can be produced, offering a range of possible future states rather than a single deterministic prediction. Studies have shown that EPFs can significantly improve the accuracy and reliability of flood forecasts by accounting for the uncertainty in precipitation inputs [14; 15; 16; 17]. Uncertainties in ensemble hydrological forecasts primarily originate from input data and the hydrological simulation process [18]. The uncertainty in hydrological simulations stems mainly from boundary conditions and the structural and parametric characteristics of the employed models [19; 20]. This paper focuses on uncertainties arising from these two sources. To quantify uncertainties in input data, utilizing multiple EPFs from various weather forecasting centers--a \"grand ensemble\" (GE)--has proven effective [21]. A GE assimilates a wider array of uncertainty sources in the inputs, creating a more robust forecast probability density function (PDF) [22]. Conversely, a single EPF may address uncertainties specific to NWP models, potentially overlooking extreme events [23]. Since 2006, the THORPEX Interactive Grand Global Ensemble (TIGGE), part of the THORPEX scientific program launched by the World Meteorological Organization, has gathered ensemble forecasts from over 10 global NWP centers [24]. The study by [PERSON], [PERSON] [23] demonstrates that forecasts from a GE capture flood peaks more effectively than those based on a single EPF, providing more reliable predictions. A single hydrological model, however, does not encompass all potential output errors nor does it capture every aspect of the flood hydrological process [25]. Therefore, employing multiple hydrological models with varied structures helps quantify model structure uncertainty while enhancing forecasting capability and accuracy [26; 27]. Recently, ensemble forecasting integrating multiple hydrological models has gained prominence [28; 29; 30]. Addressing uncertainties in both input data and hydrological model structures can be effectively managed by combining various EPF and multiple hydrological models (\"EPF + HM\").