Accurately and reliably predicting droughts under multiple models of Global Climate Models (GCMs) is a challenging task. To address this challenge, the Multimodel Ensemble (MME) method has become a valuable tool for merging multiple models and producing more accurate forecasts. This paper aims to enhance drought monitoring modules for the twenty-first century using multiple GCMs. To achieve this goal, the research introduces a new weighing paradigm called the Multimodel Homo-min Pertinence-max Hybrid Weighted Average (MHmPmHWAR) for the accurate aggregation of multiple GCMs. Secondly, the research proposes a new drought index called the Condensed Multimodal Multi-Scalar Standardized Drought Index (CMMSDI). To assess the effectiveness of MHmPmHWAR, the research compared its findings with the Simple Model Average (SMA). In the application, eighteen different GCM models of the Coupled Model Intercomparison Project Phase 6 (CMIP6) were considered at thirty-two grid points of the Tibet Plateau region. Mann–Kendall (MK) test statistics and Steady States Probabilities (SSPs) of Markov chain were used to assess the long-term trend in drought and its classes. The analysis of trends indicated that the number of grid points demonstrating an upward trend was significantly greater than those displaying a downward trend in terms of spatial coverage, at a significance level of 0.05. When examining scenario SSP1-2.6, the probability of moderate wet and normal drought was greater in nearly all temporal scales than other categories. The outcomes of SSP2-4.5 demonstrated that the likelihoods of moderate drought and normal drought were higher than other classifications. Additionally, the results of SSP5-8.5 were comparable to those of SSP2-4.5, underscoring the importance of taking effective actions to alleviate drought impacts in the future. The results demonstrate the effectiveness of the MHmPmHWAR and CMMSDI approaches in predicting droughts under multiple GCMs, which can contribute to effective drought monitoring and management.

在全球气候模型（GCM）的多个模型下准确可靠地预测干旱是一项具有挑战性的任务。为了应对这一挑战，多模型集成 (MME) 方法已成为合并多个模型并产生更准确预测的宝贵工具。本文旨在使用多个 GCM 增强二十一世纪的干旱监测模块。为了实现这一目标，该研究引入了一种新的权重范式，称为多模型 Homo-min Pertinence-max 混合加权平均 (MHmPmHWAR)，用于多个 GCM 的精确聚合。其次，研究提出了一种新的干旱指数，称为压缩多模态多标量标准化干旱指数（CMMSDI）。为了评估 MHmPmHWAR 的有效性，该研究将其研究结果与简单模型平均值 (SMA) 进行了比较。在应用中，考虑了耦合模型比对项目第六阶段（CMIP6）的18个不同的GCM模型在青藏高原地区的32个网格点。曼-肯德尔（MK）检验统计量和马尔可夫链的稳态概率（SSP）用于评估干旱及其类别的长期趋势。趋势分析表明，空间覆盖度呈上升趋势的网格点数量显着多于呈下降趋势的网格点数量，显着性水平为0.05。在检查情景 SSP1-2.6 时，几乎在所有时间尺度上中度潮湿和正常干旱的概率都大于其他类别。 SSP2-4.5的结果表明，中度干旱和正常干旱的可能性高于其他分类。此外，SSP5-8.5的结果与SSP2-4.5的结果相当，强调了采取有效行动减轻未来干旱影响的重要性。结果证明了 MHmPmHWAR 和 CMMSDI 方法在多个 GCM 下预测干旱的有效性，这有助于有效的干旱监测和管理。