To estimate the probable maximum precipitation (PMP) in a changing climate, this study proposes a new PMP estimation framework based on weather research forecasting (WRF) initialed with temperature (predicted by post-processing) for changing climate conditions. First, in order to determine temperature disturbance influencing PMP under climate change, a random forest (RF) model considering error correction is introduced to predict the temperature in the future. Results show that the revised RF model could improve accuracy in temperature prediction. Furthermore, numerical experiments of disturbance amplification of three factors (humidity, wind speed, and temperature) using the WRF model are conducted. This new scheme could consider the effect of three elements (horizontal range, vertical layer, and ratio) of influencing factors’ maximization on PMP. Results indicate that for the most unfavorable precipitation scenario of each factor magnification, the combination of three elements is different. Then, the joint amplification numerical experiments of three factors proved the existence of their interactions when multi-factors changed simultaneously. Finally, this method was tested in a high-mountain basin, the Upper Nujiang River Basin. Results showed that the increase of wind speed plays a leading role in rainfall enhancement, and the rising of relative humidity and temperature has a certain disturbance effect on rainfall. © 2022 The Authors.
