Dear Naveen,

When we are doing bias correction of future time slice, by quantile mapping,we assume that the future CDF of GCM data is same as the historical GCM CDF and then do the matching of it with CDF of observations to do correction.

But my question is if the Future GCM sample period is different from historical sample period.will this not have a bearing on bias correction as the sample size will alter the parameters of the CDF. In other ways can we do bias correction of GCM data for the entire period from 2010 to 2099 in a single stretch if we have historical data both for observations and GCM data for a period of 1970 to 1999 or we have to do it seperately for three slices 2010 to 2039, 2040 to 2070 and 2070 to 2099.

Either way you will get the same results.

In other ways my question is

1.If we have a SVM model fitted for regression with daily data for climate change impact on a variable from 1951 to 1980.The model parameters are dependent on sample size of predictors.Can the model be used to predict climate change for the entire stretch at a single go 2010 to 2099 ? or be used for predictions for three different slices of equal lengths as that of historical period?

Yes. You can do it in a single go.

2.If we calibrate a hydrologic model SWAT for 10 years and validate it for 3 years.Should it be used for prediction for period 2010 to 2099 at a go .

You can do it. However, if you can increase your calibration/validation period, it will be good. Ten years is too small period to check for trends in data. You might be knowing that there are some frequencies in rainfall of around 7 years also. If you can increase your calibration/validation period upto 30 years it will be better.

In short I am not asking about the Stationarity assumption of climate change .I am asking for the practicle effect of sample size on predictions.Is there any thing like the period of prediction is less than the calibration period as in the case of independent validation for example: 70 % period for calibration-30 % period for validation.

Usually assumption of stationarity is made in this sort of analysis. To relax this assumption you will have to find out how is CDF (mean, variance, skewness and other moments) changing with time. To analyze this, you may require a longer time series. I have not done this sort of analysis. Let's see if someone working in this area comments on this.

Kindly excuse me, if my question is stupid.

No questions are stupid, if asked with clarity.