diff --git a/conf/archive_seasonal.yml b/conf/archive_seasonal.yml
index 03ed1636bc3ff5f6d03d7701afd5d83d75c4e3e8..b98d92786d296642e8c7cb71fce049b04dc25f30 100644
--- a/conf/archive_seasonal.yml
+++ b/conf/archive_seasonal.yml
@@ -24,6 +24,13 @@ gpfs:
       name: "ECMWF SEAS5 (v5.1)"
       institution: "European Centre for Medium-Range Weather Forecasts"
       src: "exp/ecmwf/system51c3s/"
+      daily_mean: {"tas":"daily_mean/tas_f6h/", "rsds":"daily/rsds_s0-24h/",
+                   "prlr":"daily/prlr_s0-24h/", "tasmin":"daily/tasmin/",
+                   "tasmax":"daily/tasmax/", "sfcWind":"daily_mean/sfcWind_f6h/",
+                   "ta300":"daily_mean/ta300_f12h/", "ta500":"daily_mean/ta500_f12h/",
+                   "ta850":"daily_mean/ta850_f12h/", "g300":"daily_mean/g300_f12h/",
+                   "g500":"daily_mean/g500_f12h/", "g850":"daily_mean/g850_f12h/",
+                   "tdps":"daily_mean/tdps_f6h/", "hurs":"daily_mean/hurs_f6h/"}
       monthly_mean: {"tas":"monthly_mean/tas_f6h/",
 		     "tasmax":"monthly_mean/tasmax_f24h/",
                      "tasmin":"monthly_mean/tasmin_f24h/",
diff --git a/conf/archive_subseasonal.yml b/conf/archive_subseasonal.yml
index a0ec9bf1a5d140ab04a04756d8720dca599d8bc9..ecfe9bcf601286e6b886c33dad5e1f582d8f8c9f 100644
--- a/conf/archive_subseasonal.yml
+++ b/conf/archive_subseasonal.yml
@@ -16,9 +16,9 @@ gpfs:
     NCEP-CFSv2:
       name: "NCEP CFSv2"
       institution: "NOAA NCEP" #?
+      src: "exp/ncep/cfs-v2/"
       srchc: "exp/ncep/cfs-v2/"
       srcfc: "exp/ncep/cfs-v2/"
-      src: "exp/ncep/cfs-v2/"
       weekly_mean: {"tas":"weekly_mean/s2s/tas_f24h/",
                     "prlr":"weekly_mean/s2s/prlr_f24h/",
                     "tasmax":"weekly_mean/s2s/tasmax_f24h/",
@@ -26,7 +26,7 @@ gpfs:
                     "sfcWind":"weekly_mean/s2s/sfcWind_f24h/",
                     "rsds":"weekly_mean/s2s/rsds_f24h/",
                     "psl":"weekly_mean/s2s/psl_f24h/"}
-      daily_mean: {"tas":"daily_mean/s2s/tas_f6h",
+      daily_mean: {"tas":"daily_mean/s2s/tas_f6h/",
                    "prlr":"daily_mean/s2s/prlr_f6h/",
                    "psl":"daily_mean/s2s/psl_f6h/"}
       nmember:
diff --git a/modules/Loading/R/dates2load.R b/modules/Loading/R/dates2load.R
index a21795865d00baae7e14e9c8b378a1341ea5a4bb..16e24647338c60eaf93d62ef41b58b34b84e387f 100644
--- a/modules/Loading/R/dates2load.R
+++ b/modules/Loading/R/dates2load.R
@@ -23,6 +23,11 @@ dates2load <- function(recipe, logger) {
   temp_freq <- recipe$Analysis$Variables$freq
   system_name <- recipe$Analysis$Datasets$System$name
   recipe <- recipe$Analysis$Time
+
+  ## TODO: May be needed for subseasonal daily
+  # if (nchar(recipe$sdate) == 8 &&) {
+  #   recipe$sdate <- substr(recipe$sdate, 5, 8)
+  # }
   if (temp_freq == "monthly_mean") {
     # hcst dates
     file_dates <- paste0(strtoi(recipe$hcst_start):strtoi(recipe$hcst_end),
@@ -37,7 +42,6 @@ dates2load <- function(recipe, logger) {
            paste("January 1993 start date is not available for", system_name,
                  "and has been removed from the list of start dates."))
     }
-    ## TODO: Add dims?
     file_dates <- .add_dims(file_dates)
   } else if (temp_freq == "weekly_mean") {
     sday <- recipe$sday_window
@@ -71,23 +75,24 @@ dates2load <- function(recipe, logger) {
                                      ftime_max = recipe$ftime_max, out = 'hcst')
   } else {
     file_dates <- paste0(strtoi(recipe$hcst_start):strtoi(recipe$hcst_end),
-                       recipe$sdate)
+                         recipe$sdate)
+    file_dates <- .add_dims(file_dates)
   }
   # fcst dates (if fcst_year empty it creates an empty object)
-  if (! is.null(recipe$fcst_year)) {
-    if (temp_freq == "monthly_mean") {
+  if (!is.null(recipe$fcst_year)) {
+    if (temp_freq == "monthly_mean" || temp_freq == "daily_mean") {
       file_dates.fcst <- paste0(recipe$fcst_year, recipe$sdate)
       file_dates.fcst <- .add_dims(file_dates.fcst)
     } else if (temp_freq == "weekly_mean") {
       file_dates.fcst <- subseas_file_dates(startdate = fcst.sdate_to_start,
-                                   n.skill.weeks = n.skill.weeks,
-                                   n.days = sday,
-                                   hcst.start = as.numeric(recipe$hcst_start),
-                                   hcst.end = as.numeric(recipe$hcst_end),
-                                   ftime_min = recipe$ftime_min,
-                                   ftime_max = recipe$ftime_max, out = 'fcst')
+                                            n.skill.weeks = n.skill.weeks,
+                                            n.days = sday,
+                                            hcst.start = as.numeric(recipe$hcst_start),
+                                            hcst.end = as.numeric(recipe$hcst_end),
+                                            ftime_min = recipe$ftime_min,
+                                            ftime_max = recipe$ftime_max, out = 'fcst')
     } else {
-      file_dates.fcst <- paste0(recipe$fcst_year, recipe$sdate)
+      stop("Unknown temporal frequency.")
     }
   } else {
     # if no fcst year is requested:
diff --git a/modules/Loading/R/load_subseasonal.R b/modules/Loading/R/load_subseasonal.R
index dba21ee1e868fab210e4158377b31c32a216ba2d..97623b0212e69b04d29eebf2577b82becb0f1b00 100644
--- a/modules/Loading/R/load_subseasonal.R
+++ b/modules/Loading/R/load_subseasonal.R
@@ -57,6 +57,10 @@ load_subseasonal <- function(recipe) {
   
   if (store.freq %in% c("daily", "daily_mean")) {
     frequency <- "daily_mean"
+    ## TODO: In subseasonal, number of members may be different depending on
+    ## frequency. Code needs to be adjusted
+    fcst.nmember <- fcst.nmember/3
+    hcst.nmember <- hcst.nmember/3
   } else if (store.freq == "weekly_mean") {
     frequency <- "weekly_mean"
   }
@@ -142,8 +146,7 @@ load_subseasonal <- function(recipe) {
   }
   
   # Convert hcst to s2dv_cube object
-  ## TODO: Give correct dimensions to $Dates
-  ## (sday, sweek, syear instead of file_date)
+  hcst_dates <- attr(hcst, "Variables")$common$time
   hcst <- as.s2dv_cube(hcst)
   # Adjust dates for models where the time stamp goes into the next month
   if (recipe$Analysis$Variables$freq == "monthly_mean") {
@@ -163,7 +166,6 @@ load_subseasonal <- function(recipe) {
                   var_dir_depends = 'var',
                   file_date = sdates$fcst,
                   time = recipe$Analysis$Time$ftime_min:recipe$Analysis$Time$ftime_max,
-
                   latitude = values(list(lats.min, lats.max)),
                   latitude_reorder = Sort(),
                   longitude = values(list(lons.min, lons.max)),
@@ -223,21 +225,38 @@ load_subseasonal <- function(recipe) {
   
   # Obtain dates and date dimensions from the loaded hcst data to make sure
   # the corresponding observations are loaded correctly.
-  dates <- as.POSIXlt(hcst$attrs$Dates)
-  # Hcst files metadata are 3 days ahead of Obs files names:
-  ## TODO: Is this true in general, or only for a specific case (NCEPv2 + ERA5)?
-  dates$mday <- dates$mday - 3
-  dates <- as.POSIXct(dates)
-  dim(dates) <- hcst$dims[c("sday", "sweek", "syear", "time")]
-  # Separate Start() call for monthly vs daily data
+  ## TODO: Generalize to other models
+  dates <- Apply(sdates$hcst,
+                 margins = list(x = c("syear", "sweek", "sday")),
+                 fun = function(x, idx_min, idx_max) {
+                   x <- as.POSIXct(x, format = "%Y%m%d", tz = "UTC") %m+% weeks(idx_min:idx_max) %m+% days(4)
+                   return(as.array(x))
+                 },
+                 output_dims = c("time"),
+                 idx_min = recipe$Analysis$Time$ftime_min - 1,
+                 idx_max = recipe$Analysis$Time$ftime_max - 1,
+                 ncores = 1)[[1]]
+  dates <- Reorder(dates, c("sday", "sweek", "syear", "time"))
+  dates_dims <- dim(dates)
+  dates <- as.POSIXct(dates, origin = "1970-01-01", tz = "UTC")
+  dates <- format(dates, "%Y%m%d")
+  dim(dates) <- dates_dims
+
+  if (any(is.na(hcst_dates))) {
+    warn(recipe$Run$logger,
+         paste("Missing files found for the hindcast, the corresponding",
+               "observations will be filled with NA values."))
+    dates[is.na(hcst_dates)] <- NA
+  }
+  
+  ## Separate Start() call for monthly vs daily data
   if (store.freq == "weekly_mean") {
-    dates_file <- format(as.Date(dates, '%Y%m%d'), "%Y%m%d")
-    dim(dates_file) <- dim(dates)
+    ## Is this needed...?
     obs <- Start(dat = obs.path,
                  var = variable,
                  var_dir = var_dir_obs,
                  var_dir_depends = 'var',
-                 file_date = dates_file,
+                 file_date = dates,
                  latitude = values(list(lats.min, lats.max)),
                  latitude_reorder = Sort(),
                  longitude = values(list(lons.min, lons.max)),
@@ -266,7 +285,7 @@ load_subseasonal <- function(recipe) {
     lubridate::minute(dates) <- 00
     # Restore correct dimensions
     dim(idxs$hcst) <- dim(dates_file)
-    
+
     obs <- Start(dat = obs.path,
                  var = variable,
                  var_dir = var_dir_obs,
diff --git a/tests/testthat/test-subseasonal_weekly.R b/tests/testthat/test-subseasonal_weekly.R
index 6e565e76ec53568f85d38156c49491d61d7d6780..bb0759ac1572a2e0c006b61507ac918a68c1ff44 100644
--- a/tests/testthat/test-subseasonal_weekly.R
+++ b/tests/testthat/test-subseasonal_weekly.R
@@ -96,6 +96,7 @@ expect_equal(
 dim(data$obs$attrs$Dates),
 dim(data$obs$attrs$load_parameters$dat1$file_date[[1]])
 )
+# Dims are: c("sday", "sweek", "syear", "time")
 expect_equal(
 data$obs$attrs$load_parameters$dat1$file_date[[1]][2, 3, , 1],
 c("19990108", "20000108", "20010108", "20020108", "20030108")
diff --git a/tests/testthat/test-subseasonal_weekly_crossval.R b/tests/testthat/test-subseasonal_weekly_crossval.R
index f7d9e8125f527f26f2831526a3609e4fbb7d7f56..1d65ce1a23b5d31e48ca354f1b3b0202c605e2bf 100644
--- a/tests/testthat/test-subseasonal_weekly_crossval.R
+++ b/tests/testthat/test-subseasonal_weekly_crossval.R
@@ -94,7 +94,7 @@ expect_equal(as.vector(skill_metrics[['rpss-set1']][, , 2, 3]),
              tolerance = 0.0001)
 
 expect_equal(as.vector(skill_metrics[['rpss-set1_significance']][, , 2, 3]),
-             c(0,0,0,0))
+             c(0, 0, 0, 0))
 
 })