The aim of this paper is to investigate the ability of artificial neural network (ANN) models for post-processing the monthly precipitation forecasts under North American multi-model ensemble (NMME) project and proposing a new multi-model ensemble neural network (MME-NN) model. Monthly precipitation hindcasts of eight models from NMME project are considered in this study. Multi-layer perceptron neural networks are employed for post-processing the output of the models in comparison with PERSIANN-CDR climatology data. Also, utilizing a multi-criteria decision-making approach, NMME models are ranked for each month. The study is implemented over Iran and detailed discussions are provided. The results indicate that the skill of NMME models is different for each month and for each region of the country. Also, it is shown that the neural network outperforms all NMME models for all months. By using the ranking of the models, for each month, the NMME models are ordered based on their skill and a monthly rank is devoted for each model.

本文的目的是研究人工神经网络（ANN）模型对北美多模型集合（NMME）项目下的月降水量预报进行后处理的能力，并提出一种新的多模型集合神经网络（MME- NN）模型。这项研究考虑了NMME项目的8个模型的月降水后预报。与PERSIANN-CDR气候数据相比，采用多层感知器神经网络对模型的输出进行后处理。此外，利用多准则决策方法，每月对NMME模型进行排名。该研究是在伊朗进行的，并提供了详细的讨论。结果表明，该国家每个月和每个地区的NMME模型的技能都不同。也，结果表明，神经网络在所有月份中均优于所有NMME模型。通过使用模型的排名，每个月，NMME模型将根据其技能进行排序，并且每个模型每月都会排名。