<p>Multivariate drought prediction analysis is of great significance for drought disaster resistance. In this study, based on the derivation of monthly time-variant drought indicators SPI<i>t</i>, STI<i>t</i> and SSMI<i>t</i> by means of GAMLSS method, the conditional variables and their optimal combination structure were recognized through correlation analysis between targeted agricultural drought prediction variable SSMI<i>t</i>_<i>i</i>-1 and previous 1–12 monthly drought indicators SPI<i>t</i>_<i>i</i>-1-<i>n</i>, STI<i>t</i>_<i>i</i>-1-<i>n</i> and SSMI<i>t</i>_<i>i</i>-1-<i>n</i>, then the response relationship between combined distribution of conditional variable under different patterns and occurring probability of agricultural drought events was revealed through C-Vine Copulas function and conditional quantile regression method, and finally the CQRM approach based on C-Vine Copulas function of agricultural drought prediction analysis was proposed, which was verified through its application in northern Anhui Province area, China. It can be summarized from the application results that, (1) generally, previous 1–3 monthly-scale drought indicators SPI<i>t</i>_<i>i</i>-1-<i>n</i>, STI<i>t</i>_<i>i</i>-1-<i>n</i> and SSMI<i>t</i>_<i>i</i>-1-<i>n</i> can be utilized as primary conditional variables of agricultural drought prediction analysis, and the response time of drought propagation process to diverse conditional variables in summer and autumn (less than 1&#xa0;month) is relatively shorter in comparative with that of winter and spring (nearly 3&#xa0;months). (2) the utilization of more conditional variables is not definitely beneficial for the enhancement of agricultural drought prediction accuracy, and it is crucial to recognize effective monthly conditional variables and also construct its optimal combination structure through correlation analysis to improve the overall fitting performances of agricultural drought prediction analysis.</p>