Coincident design day (CDD) means the design day that considered the simultaneous occurrence of design weather elements and the correlation between design weather data and room parameters. The application of coincident design day will make the load calculation more accurate in the air-conditioning system design. However, since there are excessive possible combinations of the room parameters in practical engineering applications, it is an urgent problem to predict or match the CDD of designed room from existing/typical CDD set. In this study, a CDD prediction method based on Support Vector Machine and decision chain is proposed to solve this problem. The practicality of the prediction method was verified by evaluating its performance. The test results for Hong Kong show that average 81.71 % cases are within ±1.0 % of the deviation between the design cooling load calculated by predicted CDD and actual design cooling load, 98.02 % cases are in within ±3.0 %, and 99.33 % cases are within ±5.0 %. The verifying results of Changsha show that average 82.16 % cases are within ±1.0 % of the deviation, 97.58 % cases are within ±3.0 %, and 99.53 % cases are within ±5.0 %. These indicate that the decision chain method is practical in engineering applications. This study filled the gap of the CDD application, which may provide a basis and some inspiration for subsequent studies on CDD.