<p>Teleconnections between large-scale climate indices and regional meteorological drought are highly important for drought prediction and prevention. However, methods based on causal analysis are rarely employed. This study investigated the causal effects of six large-scale climate indices on meteorological drought in the Yellow River Basin (YRB) using transfer entropy (TE). The rotated empirical orthogonal function (REOF) was employed to identify patterns of meteorological drought described by the standardized precipitation evapotranspiration index (SPEI) from 1961 to 2022. By analyzing the results of Pearson correlation, linear Granger causality (GC), nonlinear GC, convergent cross mapping (CCM), Gaussian TE, and Kraskov-Stögbauer-Grassberger (KSG) TE, and summarizing the local KSG TE by spring, summer, autumn, winter, local peak of SPEI principal components (PCs), local valley of SPEI PCs, and the periods 1963–1982, 1983–2002, and 2003–2022, three important findings were highlighted. First, the results from the six compared methods exhibited substantial differences, but the KSG TE results were deemed the most credible due to the nonlinearity of its algorithm, its stability, and its fewer false detections. Second, each of the six climate indices exerted causal influences on meteorological drought in the YRB, but the time required for their effects to propagate and the specific subregions impacted varied. Third, the observed trends in several local TE analyses suggested that the causality could be nonstationary. The causal impacts of the same climate indices on meteorological drought varied significantly across the different aggregate times. These findings offer new perspectives on the influence of global climate indices on regional meteorological drought.</p>