Why does climate4R use too many cpu? TOO SLOW

[louwangzhiyuwhy]

options(java.parameters = "-Xmx8g")

library(climate4R.UDG)
library(loadeR)
library(loadeR.2nc)
library(transformeR)
library(climate4R.datasets)
library(downscaleR)
library(visualizeR)
library(VALUE)
library(climate4R.value)

vars <- c("var151","var165","var166") #psl; uas; vas
varp <- c("var131@85000","var132@85000","var129@50000") #131-ua; 132-va; 130-ta; 129-zg;
grid.list <- lapply(vars, function(x) {
loadGridData(dataset =
"/home/inspur/working/climate4r/ERA-I/box_surface_interim_1979_2018.nc",
var = x,
years = 1990:2018)
}
)
grid.listp <- lapply(varp, function(x) {
loadGridData(dataset =
"/home/inspur/working/climate4r/ERA-I/box_pressure_interim_1979_2018.nc",
var = x,
years = 1990:2018)
}
)
pred <- downscaleCV(xs, wsobs, folds = 3, sampling.strategy = "kfold.chronological",
scaleGrid.args = list(type = "standardize"),
method = "GLM",
prepareData.args = list(
"spatial.predictors" = list(which.combine = getVarNames(xs), v.exp = 0.9)))

the downscaleCV method uses 12603% of one CPU and TOO SLOW why?
A 30*40 box of ERA-I dataset was used to downscaling dataset is small enough why take so many resource???
here is the cenos7 top result:
PID USER PR NI VIRT RES SHR S %CPU %MEM TIME+ COMMAND
553757 inspur 20 0 105.6g 74.1g 28200 R 12603 7.4 440:22.97 R