diff --git a/R/zzz.R b/R/zzz.R
index 0579b59d48a0dfe90761c8fe081625758fbf944f..381c9d3ac69ce95bc4d1b8aa6c68bde291e5437c 100644
--- a/R/zzz.R
+++ b/R/zzz.R
@@ -692,9 +692,11 @@ generate_sub_array_of_fri <- function(with_transform, goes_across_prime_meridian
         } else if (start_padding < beta) {
           # left side too close to border, need to go to right side
           sub_array_of_fri <- c((first_index - start_padding):(last_index + end_padding), (n - (beta - start_padding - 1)):n)
+          sub_array_of_fri <- unique(sub_array_of_fri)
         } else if (end_padding < beta) {
           # right side too close to border, need to go to left side
           sub_array_of_fri <- c(1: (beta - end_padding), (first_index - start_padding):(last_index + end_padding))
+          sub_array_of_fri <- unique(sub_array_of_fri)
         }
       }
 
@@ -706,6 +708,7 @@ generate_sub_array_of_fri <- function(with_transform, goes_across_prime_meridian
       }
     }
   }
+  
   if (print_warning) {
     .warning(paste0("Adding parameter transform_extra_cells =  ", beta,
                     " to the transformed index excesses ",
diff --git a/tests/testthat/test-Start-transform-border.R b/tests/testthat/test-Start-transform-border.R
index a84f1eca315b5929cc57da45ba3f305bbc6cdbb9..dee8b4e0582e007057df58d7deeeafbc6d66cd84 100644
--- a/tests/testthat/test-Start-transform-border.R
+++ b/tests/testthat/test-Start-transform-border.R
@@ -1,4 +1,6 @@
 context("Transform: check with cdo")
+
+##############################################
 # This unit test checks different border situations: normal regional that doesn't touch the borders,
 # global situation that uses all the grids, or one side reaches the border.
 
@@ -24,6 +26,7 @@ context("Transform: check with cdo")
 
 # The result of cdo is from CDO version 1.9.8.
 
+##############################################
 test_that("1. normal regional situation", {
 
 lons.min <- 10
@@ -75,6 +78,7 @@ tolerance = 0.0001
 ) 
 
 })
+#---------------------------------------------
 
 # The result is consistent with cdo result, using all the grid points to transform
 # and crop the region.
@@ -86,8 +90,7 @@ tolerance = 0.0001
 #[4,] 284.1387 287.3716 287.7389
 #[5,] 285.6547 285.0194 286.1099
 
-#------------------------------------------------
-
+##############################################
 test_that("2. global situation", {
 
 lons.min <- 0
@@ -145,6 +148,7 @@ tolerance = 0.0001
 
 })
 
+#---------------------------------------------
 #> drop(arr2$data_array)[,1:3]
 #         [,1]     [,2]     [,3]
 #[1,] 286.4231 288.0916 285.7435
@@ -160,8 +164,7 @@ tolerance = 0.0001
 #[4,] 288.6723 285.0679 279.6016
 #[5,] 286.8253 282.6013 279.5081
 
-#-----------------------------------------------
-
+##############################################
 test_that("3. left side too close to border", {
 
 lons.min <- 0
@@ -229,7 +232,7 @@ tolerance = 0.0001
 
 })
 
-
+#---------------------------------------------
 #drop(arr2$data_array)[,]
 #         [,1]     [,2]     [,3]     [,4]     [,5]     [,6]
 #[1,] 286.4231 288.0916 285.7435 284.9907 285.4820 286.1208
@@ -239,7 +242,7 @@ tolerance = 0.0001
 #[5,] 284.3575 284.8728 284.6408 285.6547 285.0194 286.1099
 
 
-
+##############################################
 test_that("4. right side too close to border", {
 
 lons.min <- 350
@@ -287,6 +290,7 @@ tolerance = 0.0001
 
 })
 
+#---------------------------------------------
 #> drop(arr2$data_array)[,]
 #         [,1]     [,2]
 #[1,] 286.3033 285.9373
@@ -295,8 +299,7 @@ tolerance = 0.0001
 #[4,] 285.0679 279.6016
 #[5,] 282.6013 279.5081
 
-#--------------------------------------------------
-
+##############################################
 test_that("5. across meridian", {
 
 lons.min <- 170
@@ -359,6 +362,8 @@ tolerance = 0.0001
 
 })
 
+#---------------------------------------------
+
 # cdo result is [170:190]; Start() is [-180:-170; 170:180].
 # So drop(exp)[, 1:3] == drop(arr2$data_array)[, 3:5].
 #drop(arr2$data_array)[,]
@@ -370,6 +375,7 @@ tolerance = 0.0001
 #[5,] 284.6429 284.6293 284.9630 285.7766 286.0924
 
 
+##############################################
 test_that("6. normal case; [-180, 180]", {
 # The lon range is too close to border for the original longitude [0, 360], but 
 # is normal case for [-180, 180]. In zzz.R, it is counted as normal case, and the
@@ -439,9 +445,8 @@ tolerance = 0.0001
 
 })
 
-#----------------------------------------------------------
-
-test_that("6. left side too close to border; [-180, 180]", {
+##############################################
+test_that("7. left side too close to border; [-180, 180]", {
 # The lon range is too close to border for the original longitude [0, 360], but 
 # is normal case for [-180, 180]. In zzz.R, it is counted as normal case, and the
 # result is the same as 3.
@@ -490,6 +495,7 @@ tolerance = 0.0001
 
 })
 
+#---------------------------------------------
 # cdo result is [181:190]
 #> drop(arr2$data_array)[,]
 #         [,1]     [,2]
@@ -499,3 +505,208 @@ tolerance = 0.0001
 #[4,] 289.5334 289.5931
 #[5,] 285.7766 286.0924
 
+
+##############################################
+# Tests for global longitude:
+
+test_that("8. Global situation: left side too close to border; [0, 360]", {
+
+lats.min <- 0
+lats.max <- 10
+lons.min <- 0
+lons.max <- 359
+
+suppressWarnings(
+  exp <- Start(dat = '/esarchive/exp/ecmwf/system5_m1/monthly_mean/$var$_f6h/$var$_$sdate$.nc',
+               var = 'tas',
+               sdate = '20000101',
+               ensemble = indices(1),
+               time = indices(1),
+               latitude = values(list(lats.min, lats.max)),
+               latitude_reorder = Sort(),
+               longitude = values(list(lons.min, lons.max)),
+               longitude_reorder = CircularSort(0, 360),
+               transform = CDORemapper,
+               transform_params = list(grid = 'r360x180',
+                                       method = 'bilinear'),
+               transform_vars = c('latitude', 'longitude'),
+               synonims = list(latitude = c('lat','latitude'),
+                               longitude = c('lon','longitude')),
+               return_vars = list(latitude = NULL,
+                                  longitude = NULL,
+                                  time = 'sdate'),
+               retrieve = TRUE)
+)
+
+expect_equal(
+  dim(drop(exp)),
+  c(latitude = 10, longitude = 360)
+)
+expect_equal(
+  drop(exp)[,1],
+  c(299.8538, 300.0350, 300.2633, 300.4800, 300.6892, 300.2321, 300.1185,
+    301.1927, 300.3126, 300.6474),
+  tolerance = 0.0001
+)
+expect_equal(
+  drop(exp)[,2],
+  c(299.9356, 300.0557, 300.2042, 300.4571, 300.7495, 300.6388, 299.6622,
+    299.2109, 299.4723, 299.5299),
+  tolerance = 0.0001
+)
+expect_equal(
+  drop(exp)[,3],
+  c(300.0876, 300.1628, 300.2939, 300.5117, 300.6660, 300.5703, 300.0838,
+    300.3170, 299.9515, 299.7573),
+  tolerance = 0.0001
+)
+expect_equal(
+  drop(exp)[,4],
+  c(300.1547, 300.2518, 300.3484, 300.4062, 300.5299, 300.5294, 300.2701,
+    300.1524, 299.4566, 299.0317),
+  tolerance = 0.0001
+)
+expect_equal(
+  drop(exp)[,5],
+  c(300.1340, 300.1652, 300.3981, 300.5044, 300.5583, 300.5028, 300.4284,
+    299.6214, 299.0601, 299.1104),
+  tolerance = 0.0001
+)
+
+})
+
+#---------------------------------------------
+# The result is consistent with cdo result, using all the grid points to transform
+# and crop the region:
+
+# path <- "/esarchive/exp/ecmwf/system5_m1/monthly_mean/tas_f6h/tas_20000101.nc"
+# file <- NcOpen(path)
+# arr <- NcToArray(file,
+#                 dim_indices = list(time = 1, ensemble = 1,
+#                                    latitude = 1:640, longitude = 1:1296),
+#                 vars_to_read = 'tas')
+# lats <- NcToArray(file,
+#                  dim_indices = list(latitude = 1:640), vars_to_read = 'latitude')
+# lons <- NcToArray(file,
+#                  dim_indices = list(longitude = 1:1296), vars_to_read = 'longitude')
+# NcClose(file)
+
+# arr3 <- s2dv::CDORemap(arr, lons = as.vector(lons), lats = as.vector(lats),
+#                       grid = 'r360x180', method = 'bilinear', crop = c(0,359,0,10))
+
+# drop(arr3$data_array)[,1:5]
+
+#           [,1]     [,2]     [,3]     [,4]     [,5]
+#  [1,] 299.8538 299.9356 300.0876 300.1547 300.1340
+#  [2,] 300.0350 300.0557 300.1628 300.2518 300.1652
+#  [3,] 300.2633 300.2042 300.2939 300.3484 300.3981
+#  [4,] 300.4800 300.4571 300.5117 300.4062 300.5044
+#  [5,] 300.6892 300.7495 300.6660 300.5299 300.5583
+#  [6,] 300.2321 300.6388 300.5703 300.5294 300.5028
+#  [7,] 300.1185 299.6622 300.0838 300.2701 300.4284
+#  [8,] 301.1927 299.2109 300.3170 300.1524 299.6214
+#  [9,] 300.3126 299.4723 299.9515 299.4566 299.0601
+# [10,] 300.6474 299.5299 299.7573 299.0317 299.1104
+
+##############################################
+test_that("9. Global situation: right side too close to border; [0.5, 359.9]", {
+
+lats.min <- 0
+lats.max <- 10
+lons.min <- 0.5
+lons.max <- 359.9
+
+suppressWarnings(
+exp <- Start(dat = '/esarchive/exp/ecmwf/system5_m1/monthly_mean/$var$_f6h/$var$_$sdate$.nc',
+             var = 'tas',
+             sdate = '20000101',
+             ensemble = indices(1),
+             time = indices(1),
+             latitude = values(list(lats.min, lats.max)),
+             latitude_reorder = Sort(),
+             longitude = values(list(lons.min, lons.max)),
+             longitude_reorder = CircularSort(0, 360),
+             transform = CDORemapper,
+             transform_params = list(grid = 'r360x180',
+                                     method = 'bilinear'),
+             transform_vars = c('latitude', 'longitude'),
+             synonims = list(latitude = c('lat','latitude'),
+                             longitude = c('lon','longitude')),
+             return_vars = list(latitude = NULL,
+                                longitude = NULL,
+                                time = 'sdate'),
+             retrieve = TRUE)
+)
+
+
+expect_equal(
+  dim(drop(exp)),
+  c(latitude = 10, longitude = 359)
+)
+expect_equal(
+  drop(exp)[,1],
+  c(299.9356, 300.0557, 300.2042, 300.4571, 300.7495, 300.6388, 299.6622,
+    299.2109, 299.4723, 299.5299),
+  tolerance = 0.0001
+)
+expect_equal(
+  drop(exp)[,2],
+  c(300.0876, 300.1628, 300.2939, 300.5117, 300.6660, 300.5703, 300.0838,
+    300.3170, 299.9515, 299.7573),
+  tolerance = 0.0001
+)
+expect_equal(
+  drop(exp)[,3],
+  c(300.1547, 300.2518, 300.3484, 300.4062, 300.5299, 300.5294, 300.2701,
+    300.1524, 299.4566, 299.0317),
+  tolerance = 0.0001
+)
+expect_equal(
+  drop(exp)[,4],
+  c(300.1340, 300.1652, 300.3981, 300.5044, 300.5583, 300.5028, 300.4284,
+    299.6214, 299.0601, 299.1104),
+  tolerance = 0.0001
+)
+expect_equal(
+  drop(exp)[,5],
+  c(300.0874, 300.2558, 300.4289, 300.5256, 300.6006, 300.6316, 299.5125,
+    298.8563, 299.5071, 300.0644),
+  tolerance = 0.0001
+)
+
+})
+#---------------------------------------------
+
+# The result is consistent with cdo result, using all the grid points to transform
+# and crop the region:
+
+# library(easyNCDF)
+# path <- "/esarchive/exp/ecmwf/system5_m1/monthly_mean/tas_f6h/tas_20000101.nc"
+# file <- NcOpen(path)
+# arr <- NcToArray(file,
+#                 dim_indices = list(time = 1, ensemble = 1,
+#                                    latitude = 1:640, longitude = 1:1296),
+#                 vars_to_read = 'tas')
+# lats <- NcToArray(file,
+#                  dim_indices = list(latitude = 1:640), vars_to_read = 'latitude')
+# lons <- NcToArray(file,
+#                  dim_indices = list(longitude = 1:1296), vars_to_read = 'longitude')
+# NcClose(file)
+
+# arr3 <- s2dv::CDORemap(arr, lons = as.vector(lons), lats = as.vector(lats),
+#                       grid = 'r360x180', method = 'bilinear', crop = c(0.5,359.9,0,10))
+
+# drop(arr2$data_array)[,1:5]
+
+#           [,1]     [,2]     [,3]     [,4]     [,5]
+#  [1,] 299.9356 300.0876 300.1547 300.1340 300.0874
+#  [2,] 300.0557 300.1628 300.2518 300.1652 300.2558
+#  [3,] 300.2042 300.2939 300.3484 300.3981 300.4289
+#  [4,] 300.4571 300.5117 300.4062 300.5044 300.5256
+#  [5,] 300.7495 300.6660 300.5299 300.5583 300.6006
+#  [6,] 300.6388 300.5703 300.5294 300.5028 300.6316
+#  [7,] 299.6622 300.0838 300.2701 300.4284 299.5125
+#  [8,] 299.2109 300.3170 300.1524 299.6214 298.8563
+#  [9,] 299.4723 299.9515 299.4566 299.0601 299.5071
+# [10,] 299.5299 299.7573 299.0317 299.1104 300.0644
+##############################################
\ No newline at end of file