In bias correction (Quantile Mapping), what should I do for climatic variables using R (qmap package)?

I have used the "QMAP" (Quantile Mapping) package in R for downscaling of future rainfall and temperature projections. I have an observed data period of 1980-2010 and I want to use it for downscaling the future variables. But when I run the package, the output data of the package has the same characteristics as the observed data. Do you know how can I apply "QMAP" to downscale future data with my observed data?

I have attached a file for more transparency.

Regards

I am trying to do bias correction for rainfall data using the ‘qmap’ package in R.

My daily observed data is collected from 1981 to 2014 at the point (station) scale. To downscale the future data, I extracted the variable for corresponding locations from GCMs, including historical and future period (1981-2100).

The observed and GCMs data are detected the relationship and estimate the parameters of downscaling future data. But when I do that using qmap package, the results are poor. The output data is aggregated to monthly scale, and evaluated the performance of different GCMs with root mean squared error (RMSE) and correlation coefficient (r). From the results, I found that the bias correction even decreased the r. Additionally, I also provide the boxplot for month and annual precipitation. The above results are based on the fitQmapRQUANT method in the ‘qmap’ package. I also tried to other methods (fitQmapDIST, fitQmapPTF, fitQmapQUANT, and fitQmapSSPLIN), however, the results are still poor.

Does anyone get such results or I am doing it in the wrong way? How can I apply "qmap" to downscale daily precipitation data with my observed data?

Thanks in advance

Dear Sir/Madam

I would like to know the process that I have done to convert monthly precipitation of kg/m2/s1 into mm/month is right or wrong?

Here I took 19 global models from CMIP5. I made the values less than 0 like 10^-15 those are zeros by using this

ncap2 -s "where(pr <= 1.e-30f) pr=0" infile outfile1

After that multiplied with 86400.

cdo mulc,86400 outfile1 outfile2

Since I need each month individually, hence

cdo -mulc,31 -selmon,1 outfile2 outfile3

Kindly let me know the above calculations are correct or not?

If i have 20 years of past data, then can i apply quantile mapping in R language to whole future data upto 2099 by making only one future file or i have to make 4, 5 files of 20 years of future data and apply quantile mapping separately? Is there any relation between number of years in past data and and number if years in future data on which Quantile mapping is to b applied?

can anyone help me in doing Bias correction such as Quantile mapping using climate data in python ?

I want to compare ERA Interim precipitation estimates with ground measurements so i do not know to convert the data from kg/m2/s to mm/day.

Thanks in advance

Nkiaka

 I want to correct bias in RCM using the Quantile mapping. Just wondering if someone have tried Quantile mapping in PYTHON.

Additionally, I tried Delta method for the bias correction it was unable represent the extreme. Is this problem common with delta method?

Can anyone help me in doing such as downscaling using climate data in python?

What is Regridding and how we can do regridding of netcdf files through grads?