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diff --git a/2019_downscaleR_EMS.Rmd → 2019_downscaleR_GMD.Rmd b/2019_downscaleR_EMS.Rmd → 2019_downscaleR_GMD.Rmd
@@ -1,36 +1,37 @@
 ---
-title: "Statistical downscaling with `climate4R`: Contribution to the VALUE Intercomparison experiment"
-subtitle: "Paper notebook - submitted to Environmental Modelling & Software"
+title: "Statistical downscaling with package `downscaleR`: Contribution to the VALUE intercomparison experiment"
+subtitle: "Paper notebook - submitted to Geoscientific Model Development Discussions"
 author: "J. Bedia, J. Baño-Medina, M.N. Legasa, M. Iturbide, R. Manzanas, S. Herrera, D. San Martín, A.S. Cofiño & J. M Gutiérrez"
-date: "`r Sys.Date()`"
-encoding: "UTF8"
-urlcolor: blue
+date: '`r Sys.Date()`'
 output:
   pdf_document:
-    highlight: pygments  
-    toc: yes
     fig_caption: yes
-    pandoc_args: [
-      "--number-sections",
-      "--number-offset=0"]
+    highlight: pygments
     latex_engine: pdflatex
+    pandoc_args:
+    - --number-sections
+    - --number-offset=0
+    toc: yes
   html_document:
     fig_caption: yes
     highlight: pygments
     number_sections: yes
     theme: readable
     toc: yes
     toc_float: yes
+encoding: UTF8
 documentclass: article
 abstract: The R package `downscaleR` for statistical downscaling of climate information (SD) covers the most popular approaches (Model Output Statistics –including the so called "bias correction" methods– and Perfect Prognosis) and state-of-the-art techniques. It has been conceived to work primarily with daily data and can be used in the framework of both seasonal forecasting and climate change studies. Its full integration within the [climate4R](http://www.meteo.unican.es/climate4r) framework (Iturbide _et al._ 2019) makes possible the development of end-to-end downscaling applications, from data retrieval to model building, validation and prediction, bringing to climate scientists and practitioners a unique comprehensive framework for SD model development. This notebook reproduces the results presented in the paper, showcasing the main characteristics and functioning of perfect-prog downscaling with `climate4R`, including its integration with the VALUE validation framework (Project VALUE, http://www.value-cost.eu) that allows for undertaking a comprehensive evaluation of SD methods.
+urlcolor: blue
 ---
 
 \fontfamily{cmr}
 \fontsize{11}{22}
 \selectfont
 
 
-```{r setup, include=FALSE}
+
+```{r include=FALSE}
 knitr::opts_chunk$set(echo = TRUE,
                       highlight = TRUE,
                       message = FALSE,
@@ -98,6 +99,16 @@ inval_latex_img <- function(path) {
   invalid_ext <- c('svg', 'SVG', 'GIF', 'gif')
   return(tools::file_ext(path) %in% invalid_ext)
 }
+
+```
+
+```{r, eval=TRUE, echo = FALSE, cache = FALSE}
+# library("rticles")
+# library("rmarkdown")
+# rmarkdown::draft(file = "2019_downscaleR_GMD.Rmd",
+#                  template = "copernicus_article",
+#                  package = "rticles", edit = FALSE)
+# rmarkdown::render(input = "2019_downscaleR_GMD/2019_downscaleR_GMD.Rmd")
 ```
 
 # Introduction{#intro}
@@ -517,7 +528,8 @@ This function iterates over the methods in order to calculate R01 for each of th
 
 ```{r}
 lapply(1:length(methods), function(i) {
-  valueMeasure(y, x = get(methods[i]), measure.code = "ratio", index.code = "R01")$Measure
+  valueMeasure(y, x = get(methods[i]), 
+               measure.code = "ratio", index.code = "R01")$Measure
 })
 ```
 
@@ -724,7 +736,7 @@ for (i in 1:n_regions) {
                           method = "GLM", family = Gamma(link = "log"), 
                           condition = "GE", threshold = 1,
                           prepareData.args = config.M1.M6)
-  M1cv[[i]] <- gridArithmetics(M1cv.bin, M1cv.amo, operator = "*")
+  M1cv[[i]] <- gridArithmetics(M1cv.bin,M1cv.amo,operator = "*")
 }
 ```
 
@@ -750,7 +762,7 @@ First, the specific parameter lists are defined:
 
 ```{r}
 # M1-L parameter
-config.M1L <- list(local.predictors = list(n = 4, vars = vars),
+config.M1L <- list(local.predictors = list(n = 1, vars = vars),
                   spatial.predictors = list(v.exp = .95,
                                             which.combine = vars))
 # M6-L parameters
@@ -882,7 +894,8 @@ The final configuration of predictors for M1-L (stored in the `config.M1L` list)
 ```{r}
 # Predictor configuration
 spatialList <- list(v.exp = .95, which.combine = vars)
-localList.M1L <- list(n = 4, vars = vars)
+# spatialList <- NULL
+localList.M1L <- list(n = 1, vars = vars)
 localList.M6L <- list(n = 25, vars = vars)
 # Prepare predictors for M1-L
 xy.M1La <- prepareData(x_scale, y_bin,
@@ -899,11 +912,13 @@ Once the prec citors are adequately configured, the final models are calibrated
 
 ```{r}
 # M1-L - occurrence
-model.M1La <- downscaleTrain(xy.M1La, method = "GLM", family = binomial(link = "logit"))
+model.M1La <- downscaleTrain(xy.M1La, method = "GLM", 
+                             family = binomial(link = "logit"))
 
 # M1-L - amount
-model.M1Lb <- downscaleTrain(xy.M1Lb, method = "GLM", family = Gamma(link = "log"),
-                                condition = "GE", threshold = 1)
+model.M1Lb <- downscaleTrain(xy.M1Lb, method = "GLM",
+                             family = Gamma(link = "log"),
+                             condition = "GE", threshold = 1)
 
 # M6-L - analogs
 model.M6L <- downscaleTrain(xy.M6L, method = "analogs",n.analogs = 1)
@@ -919,9 +934,9 @@ First, the historical scenario is loaded, which has the UDG identifier `CMIP5_EC
 xh <- lapply(vars, function(x) {
   loadGridData(dataset = "CMIP5_EC-EARTH_r12i1p1_historical",
                var = x,
-               lonLim = c(-10, 32),
-               latLim = c(36, 72),
-               years = 1979:2008) %>% interpGrid(new.coordinates = getGrid(x.eur))
+               lonLim = c(-10,32),
+               latLim = c(36,72),
+               years = 1979:2005) %>% interpGrid(new.coordinates = getGrid(x.eur))
 }) %>% makeMultiGrid() 
 ```
 
@@ -932,9 +947,9 @@ We repeat the process considering the UDG identifier for the RCP8.5 dataset:
 xf <- grid.list <- lapply(vars, function(x) {
   loadGridData(dataset = "CMIP5_EC-EARTH_r12i1p1_rcp85",
                var = x,
-               lonLim = c(-10, 32),
-               latLim = c(36, 72),
-               years = 1979:2008) %>% interpGrid(new.coordinates = getGrid(x.eur))
+               lonLim = c(-10,32),
+               latLim = c(36,72),
+               years = 2071:2100) %>% interpGrid(new.coordinates = getGrid(x.eur))
 }) %>% makeMultiGrid() 
 ```
 
@@ -1012,31 +1027,54 @@ load("futu.M6L.rda")
 load("hist.M1L.rda")
 load("hist.M6L.rda")
 ```
-```{r, eval = TRUE}
-delta.glm <- gridArithmetics(climatology(futu.M1L), climatology(hist.M1L), operator = "-")
-delta.an <- gridArithmetics(climatology(futu.M6L), climatology(hist.M6L), operator = "-")
-```
 
-The GLM and Analog projected anomalies are grouped in a multigrid in order to jointly display them in the map. 
+We project the change in the frequency of precipitation (i.e., R01) and in the mean wet days (SDII) computed with the function `valueIndex` of the package `climate4R.value`. This is done for the two configurations described: M1L and M6L.
 
 ```{r, eval = TRUE}
-deltas <- makeMultiGrid(delta.glm, delta.an)
+dR01.M1L <- gridArithmetics(valueIndex(futu.M1L,index.code = "R01")$Index,
+                            valueIndex(hist.M1L,index.code = "R01")$Index,
+                            operator = "-") %>% 
+  gridArithmetics(valueIndex(hist.M1L,index.code = "R01")$Index,
+                  operator = "/")
+
+dSDII.M1L <- gridArithmetics(valueIndex(futu.M1L,index.code = "SDII")$Index,
+                             valueIndex(hist.M1L,index.code = "SDII")$Index,
+                             operator = "-") %>%
+  gridArithmetics(valueIndex(hist.M1L,index.code = "SDII")$Index,
+                  operator = "/")
+dR01.M6L <- gridArithmetics(valueIndex(futu.M6L,index.code = "R01")$Index,
+                            valueIndex(hist.M6L,index.code = "R01")$Index,
+                            operator = "-") %>% 
+  gridArithmetics(valueIndex(hist.M6L,index.code = "R01")$Index,
+                  operator = "/")
+dSDII.M6L <- gridArithmetics(valueIndex(futu.M6L,index.code = "SDII")$Index,
+                             valueIndex(hist.M6L,index.code = "SDII")$Index,
+                             operator = "-") %>% 
+  gridArithmetics(valueIndex(hist.M6L,index.code = "SDII")$Index,
+                  operator = "/")
+```
+
+The GLM and Analog projected anomalies are grouped in a multigrid in order to jointly display them in the map.
+
+```{r, eval = TRUE}
+deltas <- makeMultiGrid(dR01.M6L, dSDII.M6L,
+                        dR01.M1L, dSDII.M1L)
 ```
 
 The figure is produced with `spatialPlot`. Here, we use the color palettes provided by the R package `RColorBrewer`, and several tuning parameters are passed to spatialPlot to produce the final paper figure:
 
-
 ```{r, fig.width = 9, eval = TRUE}
-colsindex <- rev(RColorBrewer::brewer.pal(n = 9, "RdBu"))
+colsindex <- RColorBrewer::brewer.pal(n = 9, "RdBu")
 cb <- colorRampPalette(colsindex)(100)
 spatialPlot(deltas, backdrop.theme = "countries",
             aspect = "iso",
             col.regions = cb,
             at = seq(-1, 1, 0.01),
             set.min = -1, set.max = 1,
             colorkey = TRUE,
-            main = "Downscaled future (2071-2100) precipitation anomalies (mm/day)",
-            strip = lattice::strip.custom(factor.levels = c("M1-L (GLM)","M6-L (Analog)"))
+            strip = lattice::strip.custom(factor.levels = 
+                                            c("M6-L (R01)","M6-L (SDII)",
+                                              "M1-L (R01)","M1-L (SDII)"))
 )
 ```
 

diff --git a/2019_downscaleR_EMS.html → 2019_downscaleR_GMD.html b/2019_downscaleR_EMS.html → 2019_downscaleR_GMD.html
diff --git a/2019_downscaleR_EMS.pdf → 2019_downscaleR_GMD.pdf b/2019_downscaleR_EMS.pdf → 2019_downscaleR_GMD.pdf
diff --git a/README.md b/README.md
@@ -10,15 +10,15 @@ With this regard, the main objective of this repository is to maintain a number
 
 An overview of the notebooks so far included and the corresponding published articles is next provided:
 
-| notebook  | Journal Article Title | DOI  	
-|---|---|---
-| 2018_visualizeR_EMS | An R package to visualize and communicate uncertainty in seasonal climate prediction | https://doi.org/10.1016/j.envsoft.2017.09.008
-| 2018_ECOMS-UDG_ClimateServices | The ECOMS User Data Gateway: Towards seasonal forecast data provision and research reproducibility in the era of Climate Services | http://dx.doi.org/10.1016/j.cliser.2017.07.001
-| 2018_fireDanger_ClimateServices | Seasonal predictions of Fire Weather Index: Paving the way for their operational applicability in Mediterranean Europe | http://dx.doi.org/10.1016/j.cliser.2017.04.001
-| 2018_climate4R_example | The R-based climate4R open framework for reproducible climate data access and post-processing | https://doi.org/10.1016/j.envsoft.2018.09.009
-| 2018_drought4R_NCOMMS | Exacerbated fires in Mediterranean Europe due to anthropogenic warming projected with non-stationary climate-fire models | https://doi.org/10.1038/s41467-018-06358-z
-| 2018_metaclip_EMS* | METACLIP: A semantic provenance framework for climate products | _under review_
-| 2019_downscaleR_EMS* | Statistical downscaling with climate4R: Contribution to the VALUE intercomparison experiment | _submitted_
+| notebook  | Article Title | Journal | DOI  	
+|---|---|---|---
+| 2018_visualizeR_EMS | An R package to visualize and communicate uncertainty in seasonal climate prediction | Environmental Modelling and Software | https://doi.org/10.1016/j.envsoft.2017.09.008
+| 2018_ECOMS-UDG_ClimateServices | The ECOMS User Data Gateway: Towards seasonal forecast data provision and research reproducibility in the era of Climate Services | Climate Services | http://dx.doi.org/10.1016/j.cliser.2017.07.001
+| 2018_fireDanger_ClimateServices | Seasonal predictions of Fire Weather Index: Paving the way for their operational applicability in Mediterranean Europe | Climate Services | http://dx.doi.org/10.1016/j.cliser.2017.04.001
+| 2018_climate4R_example | The R-based climate4R open framework for reproducible climate data access and post-processing | Environmental Modelling and Software | https://doi.org/10.1016/j.envsoft.2018.09.009
+| 2018_drought4R_NCOMMS | Exacerbated fires in Mediterranean Europe due to anthropogenic warming projected with non-stationary climate-fire models | Nature Communications | https://doi.org/10.1038/s41467-018-06358-z
+| 2018_metaclip_EMS* | METACLIP: A semantic provenance framework for climate products | Environmental Modelling and Software | https://doi.org/10.1016/j.envsoft.2019.07.005
+| 2019_downscaleR_GMD* | Statistical downscaling with downscaleR: Contribution to the VALUE intercomparison experiment | Geoscientific Model Development Discuss. | _submitted_
 
 ***
 **NOTE**: The `climate4R` packages are being actively developed. Thus, the notebooks often indicate the installation of the package versions that were used at the moment of compiling them. Alternatively, there is usually a Session Information section at the end of each notebook with details on the package versions used to compile. As a result, example code may fail if run with different versions. If you experience problems while running some of the examples, **please ensure that you are using the appropriate package versions**. If the problem persists, just let us know by dropping a ticket in the Issues section of this repo. Thank you!