This study employed Machine Learning (ML) technique known as Convolutional Autoencoder to build Statistical Downscaling Model (SDM) emulator. Eight General Circulation Models (GCMs) rainfall datasets were selected under the Representative Concentration Pathway (RCP4.5) emission scenario over Northern Africa. Historical rainfall simulation for the period 1951–2005 from 8 GCMs were applied to train/validate the SDM. To evaluate the SDM performance emulating latest Rossby Centre (RCA4) RCM, SDM results were investigated against RCM projection products (2006–2100). Continuous statistics were employed to examine the SDM performance. The SDM has exhibited positive correlation of 0.75 < R < 0.95 and low RMSE values ranging between 6.9 and 15.8 mm/month. Similarly, the bias ratio scored a low value ranging from −8.94 < bias <8.25. The SDM showed good performance in reproducing the temporal rainfall projections, whereas unsatisfactory simulation was recorded regarding the spatial rainfall projections. In conclusion, the SDM showed better performance reproducing the projections of the mean ensemble rather than the individual RCMs. For future work, the SDM could be employed to downscale the mean ensemble projections of different climate variables.

这项研究采用了称为卷积自动编码器的机器学习（ML）技术来构建统计缩减模型（SDM）仿真器。在北非代表性浓度途径（RCP4.5）排放情景下，选择了八个通用循环模型（GCM）降雨数据集。利用8个GCM对1951-2005年的历史降雨进行了模拟，以对SDM进行训练/验证。为了评估模仿最新的Rossby Center（RCA4）RCM的SDM性能，针对RCM投影产品（2006–2100）对SDM结果进行了调查。连续统计数据用于检查SDM性能。SDM表现出0.75 <R <0.95的正相关性，并且RMSE值较低，介于6.9和15.8 mm /月之间。类似地，偏置比得分较低，范围为-8.94 <bias <8.25。SDM在再现时间降雨预测中显示出良好的性能，而关于空间降雨预测的模拟记录却不理想。总之，SDM在再现平均合奏而不是单个RCM方面表现出更好的性能。对于将来的工作，可以使用SDM来缩小不同气候变量的平均总体预测。