###### Abstract
Successful applications of XGBoost that yield satisfactory forecast accuracy have been reported for various scenarios and forecast variables, including temperature, precipitation [14], wind power [15], solar radiance [6] and wave heights and periods [16], etc. In these studies, the size of the training dataset varies from the scale of \(10^{3}\) to \(10^{4}\), and training time can be as low as less than 1.0 s. The fast training of XGBoost enables hyperparameter tuning (the optimization of the parameters of a ML model for potentially best performance) to optimize model accuracy, and allows for more frequent model updating and retraining, thereby utilising the latest available data in model training. Owing to the satisfactory performance of XGBoost as already benchmarked and tested in previous studies, and its superiority of computational efficiency in training compared with deep learning models, XGBoost is selected for the current study without further model selection. With the constant development of bigger ML models, and the growing size of the datasets used to train them, the complexity of the models has also been growing significantly. This has continued to the level that the internal structure of the model is no longer easily probed and the predictions of the models are more difficult to interpret. The low interpretability leads to complex models being considered as black boxes and less trusted by customers, even if their accuracy has been validated on test datasets. The need to better understand, explain and interpret the outcome from ML models results in growing recent research interest in the field of Explainable Artificial Intelligence (XAI) [17; 18]. For weather forecasting models, it is also necessary to investigate and confirm that the causality learned by ML models agrees with that of the physics-based models and experience from operational weather forecasters. So far, various explanative methods have been proposed. On the scale of the entire dataset, it is possible to understand the ML model in terms of global feature importance. However, the disadvantage of feature importance is that it is calculated on the entire training dataset and cannot perfectly explain the reasoning of each individual output from the model. This prompts the proposal of local explanation methods, among which SHapley Additive exPlanations (SHAP) has been prominent [19]. As a model-agnostic approach, SHAP has been used for local explanations for different model architectures and the insights provided by SHAP are also used for model debugging, model health monitoring and data drift detection [20; 21]. In the area of weather forecasting, the application of SHAP or other local explanation methods is still rare, with most of the studies only presenting the evaluation metrics of the trained models. However, recent studies have started to adopt SHAP for model explanations. For example, SHAP is used to explain spatial-temporal patterns picked up by the LSTM model for drought forecasts [22; 23] and the explanations are compared with physics-based models. It should be noted that while SHAP helps explain the decision-making process towards an individual output, it does not provide insight into the correctness or accuracy of that output. The need to understand the quality of each prediction point from a ML model for better risk awareness and management prompts the study of reliable machine learning. It is understood that the imperfections of a ML model can originate from the quantity and quality of data, and model architecture [24]. With insights into data properties and ML mechanisms, it is sometimes possible to identify those individual predictions with a higher risk of being misclassified or inaccurate and therefore less reliable than a global metric implies. The principles of pointwise reliability have been proposed by [25], and various methodologies to evaluate reliability have been investigated by [26; 27; 28]. The real-time response mechanism to unreliable predictions is also integrated into some ML systems.