This supplementary material provides detailed supplementary figures and tables to support the main findings of the study on compounding effects in flood drivers and their impact on estimating extreme river floods. The key components include: 1. **Hydrometeorological Conditions**: Figures S1 to S3 provide the daily average rainfall, temperature, soil moisture, and snowpack data for 3,527 catchments from 1981 to 2020, along with climatic and local attributes such as climate moisture index, snow cover extent, catchment size, stream gradient, forest cover extent, and sand fraction in soil. 2. **Training Samples and Multi-Driver Identification**: Figures S4 to S6 illustrate the procedure for identifying multi-driver river floods using examples from Slovakia and the United Kingdom. Figure S4 shows the relationship between aggregated contributions and event-averaged magnitude of different variables, while Figure S5 details the main driving variables for annual maximum flood events. Figure S6 examines the sensitivity of using different thresholds to identify main drivers. 3. **Impact of Compounding Drivers**: Figures S7 to S9 demonstrate the impact of compounding drivers on river flood events using different thresholds (70th, 85th, and 90th percentiles) and a stricter criterion for predictive performance. Figure S10 illustrates the importance of compounding effects in two flood event samples, and Figure S11 explores the sensitivity of using different thresholds to identify main interaction effects. 4. **Flood Complexity and Estimation Methods**: Figures S12 and S13 investigate the impact of flood complexity on estimating large flood magnitudes using different methods for fitting the Generalized Extreme Value (GEV) distribution and plotting positions. Table S1 provides candidate values for hyperparameters of the LightGBM models used in the study. These supplementary materials enhance the understanding of the study's methodology and results, providing a comprehensive overview of the factors influencing extreme river floods and the effectiveness of different estimation methods.This supplementary material provides detailed supplementary figures and tables to support the main findings of the study on compounding effects in flood drivers and their impact on estimating extreme river floods. The key components include: 1. **Hydrometeorological Conditions**: Figures S1 to S3 provide the daily average rainfall, temperature, soil moisture, and snowpack data for 3,527 catchments from 1981 to 2020, along with climatic and local attributes such as climate moisture index, snow cover extent, catchment size, stream gradient, forest cover extent, and sand fraction in soil. 2. **Training Samples and Multi-Driver Identification**: Figures S4 to S6 illustrate the procedure for identifying multi-driver river floods using examples from Slovakia and the United Kingdom. Figure S4 shows the relationship between aggregated contributions and event-averaged magnitude of different variables, while Figure S5 details the main driving variables for annual maximum flood events. Figure S6 examines the sensitivity of using different thresholds to identify main drivers. 3. **Impact of Compounding Drivers**: Figures S7 to S9 demonstrate the impact of compounding drivers on river flood events using different thresholds (70th, 85th, and 90th percentiles) and a stricter criterion for predictive performance. Figure S10 illustrates the importance of compounding effects in two flood event samples, and Figure S11 explores the sensitivity of using different thresholds to identify main interaction effects. 4. **Flood Complexity and Estimation Methods**: Figures S12 and S13 investigate the impact of flood complexity on estimating large flood magnitudes using different methods for fitting the Generalized Extreme Value (GEV) distribution and plotting positions. Table S1 provides candidate values for hyperparameters of the LightGBM models used in the study. These supplementary materials enhance the understanding of the study's methodology and results, providing a comprehensive overview of the factors influencing extreme river floods and the effectiveness of different estimation methods.