diff --git a/deps/evalhyd-cpp b/deps/evalhyd-cpp
index 1d815c6808c61511864b38cee7d8b03be1187cb8..6f17c3e0f32f935c0a4f34dc1d3c6a9ee780497b 160000
--- a/deps/evalhyd-cpp
+++ b/deps/evalhyd-cpp
@@ -1 +1 @@
-Subproject commit 1d815c6808c61511864b38cee7d8b03be1187cb8
+Subproject commit 6f17c3e0f32f935c0a4f34dc1d3c6a9ee780497b
diff --git a/deps/xtensor b/deps/xtensor
index e534928cc30eb3a4a05539747d98e1d6868c2d62..44b56bbae2185ebf19e6f617ac5690344b9e35a4 160000
--- a/deps/xtensor
+++ b/deps/xtensor
@@ -1 +1 @@
-Subproject commit e534928cc30eb3a4a05539747d98e1d6868c2d62
+Subproject commit 44b56bbae2185ebf19e6f617ac5690344b9e35a4
diff --git a/environment.yml b/environment.yml
index b09e246d273533a54e0e5246739099c3f3c73370..b57f3bbf94f28d09987d2cc216e8a5229727cbc5 100644
--- a/environment.yml
+++ b/environment.yml
@@ -12,7 +12,7 @@ dependencies:
   - numpy
   - pybind11
   - xtl==0.7.5
-  - xtensor==0.24.6
+  - xtensor==0.24.7
   - xtensor-python==0.26.1
 #  - evalhyd-cpp==0.1.0
   # Test dependencies
diff --git a/tests/expected/evalp/CONT_TBL.csv b/tests/expected/evalp/CONT_TBL.csv
new file mode 100644
index 0000000000000000000000000000000000000000..4e4c900d61c3589dc1391230f89c57f9fa6ad070
--- /dev/null
+++ b/tests/expected/evalp/CONT_TBL.csv
@@ -0,0 +1,208 @@
+0.44694534,0.55305466,0.,0.
+0.33118971,0.66881029,0.,0.
+0.2733119,0.7266881,0.,0.
+nan,nan,nan,nan
+0.38585209,0.04823151,0.06109325,0.50482315
+0.28938907,0.02572347,0.04180064,0.64308682
+0.20578778,0.0192926,0.06752412,0.7073955
+nan,nan,nan,nan
+0.38585209,0.04823151,0.06109325,0.50482315
+0.28938907,0.02572347,0.04180064,0.64308682
+0.20578778,0.0192926,0.06752412,0.7073955
+nan,nan,nan,nan
+0.38585209,0.04823151,0.06109325,0.50482315
+0.28938907,0.02572347,0.04180064,0.64308682
+0.20578778,0.0192926,0.06752412,0.7073955
+nan,nan,nan,nan
+0.38585209,0.04823151,0.06109325,0.50482315
+0.28938907,0.02572347,0.04180064,0.64308682
+0.20578778,0.0192926,0.06752412,0.7073955
+nan,nan,nan,nan
+0.38585209,0.04501608,0.06109325,0.50803859
+0.28938907,0.02572347,0.04180064,0.64308682
+0.20578778,0.0192926,0.06752412,0.7073955
+nan,nan,nan,nan
+0.38585209,0.04501608,0.06109325,0.50803859
+0.28938907,0.02572347,0.04180064,0.64308682
+0.20578778,0.0192926,0.06752412,0.7073955
+nan,nan,nan,nan
+0.38585209,0.04501608,0.06109325,0.50803859
+0.28938907,0.02572347,0.04180064,0.64308682
+0.20578778,0.0192926,0.06752412,0.7073955
+nan,nan,nan,nan
+0.38585209,0.04501608,0.06109325,0.50803859
+0.28938907,0.02572347,0.04180064,0.64308682
+0.20578778,0.0192926,0.06752412,0.7073955
+nan,nan,nan,nan
+0.38585209,0.04501608,0.06109325,0.50803859
+0.28938907,0.02572347,0.04180064,0.64308682
+0.20578778,0.0192926,0.06752412,0.7073955
+nan,nan,nan,nan
+0.38585209,0.04501608,0.06109325,0.50803859
+0.28938907,0.02572347,0.04180064,0.64308682
+0.20578778,0.0192926,0.06752412,0.7073955
+nan,nan,nan,nan
+0.38585209,0.04501608,0.06109325,0.50803859
+0.28938907,0.02572347,0.04180064,0.64308682
+0.20578778,0.0192926,0.06752412,0.7073955
+nan,nan,nan,nan
+0.38585209,0.04501608,0.06109325,0.50803859
+0.28938907,0.02572347,0.04180064,0.64308682
+0.20578778,0.0192926,0.06752412,0.7073955
+nan,nan,nan,nan
+0.38585209,0.04501608,0.06109325,0.50803859
+0.28938907,0.02572347,0.04180064,0.64308682
+0.20578778,0.0192926,0.06752412,0.7073955
+nan,nan,nan,nan
+0.38585209,0.04501608,0.06109325,0.50803859
+0.28938907,0.02572347,0.04180064,0.64308682
+0.20578778,0.0192926,0.06752412,0.7073955
+nan,nan,nan,nan
+0.38585209,0.04501608,0.06109325,0.50803859
+0.28938907,0.02572347,0.04180064,0.64308682
+0.20578778,0.0192926,0.06752412,0.7073955
+nan,nan,nan,nan
+0.38585209,0.04501608,0.06109325,0.50803859
+0.28938907,0.02572347,0.04180064,0.64308682
+0.20578778,0.0192926,0.06752412,0.7073955
+nan,nan,nan,nan
+0.38585209,0.04501608,0.06109325,0.50803859
+0.28938907,0.02572347,0.04180064,0.64308682
+0.20578778,0.0192926,0.06752412,0.7073955
+nan,nan,nan,nan
+0.38585209,0.04501608,0.06109325,0.50803859
+0.28938907,0.02572347,0.04180064,0.64308682
+0.20578778,0.0192926,0.06752412,0.7073955
+nan,nan,nan,nan
+0.38585209,0.04501608,0.06109325,0.50803859
+0.28938907,0.02572347,0.04180064,0.64308682
+0.20578778,0.0192926,0.06752412,0.7073955
+nan,nan,nan,nan
+0.38585209,0.04501608,0.06109325,0.50803859
+0.28938907,0.02572347,0.04180064,0.64308682
+0.20578778,0.0192926,0.06752412,0.7073955
+nan,nan,nan,nan
+0.38585209,0.04501608,0.06109325,0.50803859
+0.28938907,0.02572347,0.04180064,0.64308682
+0.20578778,0.0192926,0.06752412,0.7073955
+nan,nan,nan,nan
+0.38585209,0.04501608,0.06109325,0.50803859
+0.28938907,0.02572347,0.04180064,0.64308682
+0.20578778,0.0192926,0.06752412,0.7073955
+nan,nan,nan,nan
+0.38585209,0.04501608,0.06109325,0.50803859
+0.28938907,0.02572347,0.04180064,0.64308682
+0.20578778,0.0192926,0.06752412,0.7073955
+nan,nan,nan,nan
+0.38585209,0.04501608,0.06109325,0.50803859
+0.28938907,0.02572347,0.04180064,0.64308682
+0.20578778,0.0192926,0.06752412,0.7073955
+nan,nan,nan,nan
+0.38585209,0.04501608,0.06109325,0.50803859
+0.28938907,0.02572347,0.04180064,0.64308682
+0.20578778,0.0192926,0.06752412,0.7073955
+nan,nan,nan,nan
+0.38585209,0.04501608,0.06109325,0.50803859
+0.28938907,0.02572347,0.04180064,0.64308682
+0.20578778,0.0192926,0.06752412,0.7073955
+nan,nan,nan,nan
+0.38585209,0.04501608,0.06109325,0.50803859
+0.28938907,0.02572347,0.04180064,0.64308682
+0.20578778,0.0192926,0.06752412,0.7073955
+nan,nan,nan,nan
+0.38585209,0.04501608,0.06109325,0.50803859
+0.28938907,0.02572347,0.04180064,0.64308682
+0.20578778,0.0192926,0.06752412,0.7073955
+nan,nan,nan,nan
+0.38585209,0.04501608,0.06109325,0.50803859
+0.28938907,0.02572347,0.04180064,0.64308682
+0.20578778,0.0192926,0.06752412,0.7073955
+nan,nan,nan,nan
+0.38585209,0.04501608,0.06109325,0.50803859
+0.28938907,0.02572347,0.04180064,0.64308682
+0.20578778,0.0192926,0.06752412,0.7073955
+nan,nan,nan,nan
+0.38585209,0.04501608,0.06109325,0.50803859
+0.28938907,0.02572347,0.04180064,0.64308682
+0.20578778,0.0192926,0.06752412,0.7073955
+nan,nan,nan,nan
+0.38585209,0.04501608,0.06109325,0.50803859
+0.28938907,0.02572347,0.04180064,0.64308682
+0.20578778,0.0192926,0.06752412,0.7073955
+nan,nan,nan,nan
+0.38585209,0.04501608,0.06109325,0.50803859
+0.28938907,0.02572347,0.04180064,0.64308682
+0.20578778,0.0192926,0.06752412,0.7073955
+nan,nan,nan,nan
+0.38585209,0.04501608,0.06109325,0.50803859
+0.28938907,0.02572347,0.04180064,0.64308682
+0.20578778,0.0192926,0.06752412,0.7073955
+nan,nan,nan,nan
+0.38585209,0.04501608,0.06109325,0.50803859
+0.28938907,0.02572347,0.04180064,0.64308682
+0.20578778,0.0192926,0.06752412,0.7073955
+nan,nan,nan,nan
+0.38585209,0.04501608,0.06109325,0.50803859
+0.28938907,0.02572347,0.04180064,0.64308682
+0.20578778,0.0192926,0.06752412,0.7073955
+nan,nan,nan,nan
+0.38585209,0.04501608,0.06109325,0.50803859
+0.28938907,0.02572347,0.04180064,0.64308682
+0.20578778,0.0192926,0.06752412,0.7073955
+nan,nan,nan,nan
+0.38585209,0.04501608,0.06109325,0.50803859
+0.28938907,0.02572347,0.04180064,0.64308682
+0.20578778,0.0192926,0.06752412,0.7073955
+nan,nan,nan,nan
+0.38585209,0.04501608,0.06109325,0.50803859
+0.28938907,0.02572347,0.04180064,0.64308682
+0.20578778,0.0192926,0.06752412,0.7073955
+nan,nan,nan,nan
+0.38585209,0.04501608,0.06109325,0.50803859
+0.28938907,0.02572347,0.04180064,0.64308682
+0.20578778,0.0192926,0.06752412,0.7073955
+nan,nan,nan,nan
+0.38585209,0.04501608,0.06109325,0.50803859
+0.28938907,0.02572347,0.04180064,0.64308682
+0.20578778,0.0192926,0.06752412,0.7073955
+nan,nan,nan,nan
+0.38585209,0.04501608,0.06109325,0.50803859
+0.28938907,0.02572347,0.04180064,0.64308682
+0.20578778,0.0192926,0.06752412,0.7073955
+nan,nan,nan,nan
+0.38585209,0.04501608,0.06109325,0.50803859
+0.28938907,0.02572347,0.04180064,0.64308682
+0.20578778,0.0192926,0.06752412,0.7073955
+nan,nan,nan,nan
+0.38585209,0.04501608,0.06109325,0.50803859
+0.28938907,0.02572347,0.04180064,0.64308682
+0.20578778,0.0192926,0.06752412,0.7073955
+nan,nan,nan,nan
+0.38585209,0.04501608,0.06109325,0.50803859
+0.28938907,0.02572347,0.04180064,0.64308682
+0.20578778,0.0192926,0.06752412,0.7073955
+nan,nan,nan,nan
+0.38585209,0.04501608,0.06109325,0.50803859
+0.28938907,0.02572347,0.04180064,0.64308682
+0.20578778,0.0192926,0.06752412,0.7073955
+nan,nan,nan,nan
+0.38585209,0.04501608,0.06109325,0.50803859
+0.28938907,0.02572347,0.04180064,0.64308682
+0.20578778,0.0192926,0.06752412,0.7073955
+nan,nan,nan,nan
+0.38585209,0.04501608,0.06109325,0.50803859
+0.28938907,0.02572347,0.04180064,0.64308682
+0.20578778,0.0192926,0.06752412,0.7073955
+nan,nan,nan,nan
+0.37942122,0.04501608,0.06752412,0.50803859
+0.28938907,0.02572347,0.04180064,0.64308682
+0.20578778,0.0192926,0.06752412,0.7073955
+nan,nan,nan,nan
+0.37942122,0.04501608,0.06752412,0.50803859
+0.28938907,0.02572347,0.04180064,0.64308682
+0.20578778,0.0192926,0.06752412,0.7073955
+nan,nan,nan,nan
+0.37942122,0.04501608,0.06752412,0.50803859
+0.28617363,0.02572347,0.04501608,0.64308682
+0.20578778,0.01607717,0.06752412,0.71061093
+nan,nan,nan,nan
diff --git a/tests/test_probabilist.py b/tests/test_probabilist.py
index c47aaf29ebf8689d3695d9ff323c952f9af2c6af..0c14b7aa3f14f08df60e8825a7cafcb34948f187 100644
--- a/tests/test_probabilist.py
+++ b/tests/test_probabilist.py
@@ -20,7 +20,7 @@ _all_metrics = (
     # quantile-based
     'QS', 'CRPS_FROM_QS',
     # contingency table-based
-    'POD', 'POFD', 'FAR', 'CSI', 'ROCSS',
+    'CONT_TBL', 'POD', 'POFD', 'FAR', 'CSI', 'ROCSS',
     # ranks-based
     'RANK_HIST', 'DS', 'AS',
     # intervals
@@ -68,8 +68,13 @@ class TestMetrics(unittest.TestCase):
         metric: (
             numpy.genfromtxt(f"./expected/evalp/{metric}.csv", delimiter=',')
             [numpy.newaxis, numpy.newaxis, numpy.newaxis, numpy.newaxis, ...]
-        ) for metric in ('POD', 'POFD', 'FAR', 'CSI', 'ROCSS')
+        ) for metric in ('CONT_TBL', 'POD', 'POFD', 'FAR', 'CSI', 'ROCSS')
     }
+    # /!\ stacked-up thresholds in CSV file for CONT_TBL
+    #     because 7D metric so need to reshape array
+    expected_ct['CONT_TBL'] = (
+        expected_ct['CONT_TBL'].reshape(expected_ct['POD'].shape + (4,))
+    )
 
     expected_rk = {
         metric: (