An analog ensemble system was developed for the realisation of local-scale surface meteorological variables for independent test data (test data) at six stations over the north-west Himalaya (NWH), India. Extreme values (the maximum value and the minimum value) and the mean value in 10 analog days (the analog mean) and the climatological mean of each surface meteorological variable were compared with its corresponding observed values on the same day ($d0$, lead time 0 hour (h)), $d1 (d0 + 1$, lead time 24 h), $d2 (d0 + 2$, lead time 48 h) and $d3 (d0 + 3$, lead time 72 h) of test data. Pearson correlation coefficients(CCs), Mean Absolute Differences (MADs) and Root Mean Square Differences (RMSDs) of the extreme values in analog days, and the analog mean and climatological mean of each meteorological variable on $d0$ with its corresponding observed values on $d0, d1, d2$ and $d3$ of test data were computed at six stations over the NWH. CCs of extreme values in analog days and the analog mean of each meteorological variable on d0 with its observed values on $d0, d1, d2$ and $d3$ were found to be higher than the CCs of the climatological mean of each meteorological variable on d0 with its observed values on $d0, d1, d2$ and $d3$. MADs (RMSDs) of extreme values in analog days and the analog mean of each meteorological variable on $d0$ with its observed values on $d0, d1, d2$ and $d3$ were found to be lesser than the MADs (RMSDs) of the climatological mean of each meteorological variable on d0 with its observed values on $d0, d1, d2$ and $d3$. However, the MADs (RMSDs) of the extreme values of each meteorological variable in analog days were found to be higher than the MADs (RMSDs) of its analog mean. Results show that the analog mean of each meteorological variable holds better predictive skill than the extreme values in analog days and its climatological mean. MADs (RMSDs) of different surface meteorological variables in surface weather analogs comparable to Mean Absolute Errors (MAEs) and RootMean Square Errors (RMSEs) for their prediction with the help of different types of weather forecast models show that the surface weather analogs hold good promise for the local-scale prediction of surface meteorological variables over the NWH.