diff --git a/webnn/conformance_tests/gpu/resample2d.https.any.js b/webnn/conformance_tests/gpu/resample2d.https.any.js
new file mode 100644
index 00000000000000..dd8e441946e1b5
--- /dev/null
+++ b/webnn/conformance_tests/gpu/resample2d.https.any.js
@@ -0,0 +1,10 @@
+// META: title=test WebNN API resample2d operation
+// META: global=window,dedicatedworker
+// META: script=../../resources/utils.js
+// META: timeout=long
+
+'use strict';
+
+// https://webmachinelearning.github.io/webnn/#api-mlgraphbuilder-resample2d-method
+
+testWebNNOperation('resample2d', buildOperationWithSingleInput, 'gpu');
\ No newline at end of file
diff --git a/webnn/conformance_tests/resample2d.https.any.js b/webnn/conformance_tests/resample2d.https.any.js
new file mode 100644
index 00000000000000..0b5b3e00320299
--- /dev/null
+++ b/webnn/conformance_tests/resample2d.https.any.js
@@ -0,0 +1,10 @@
+// META: title=test WebNN API resample2d operation
+// META: global=window,dedicatedworker
+// META: script=../resources/utils.js
+// META: timeout=long
+
+'use strict';
+
+// https://webmachinelearning.github.io/webnn/#api-mlgraphbuilder-resample2d-method
+
+testWebNNOperation('resample2d', buildOperationWithSingleInput);
\ No newline at end of file
diff --git a/webnn/resources/test_data/resample2d.json b/webnn/resources/test_data/resample2d.json
new file mode 100644
index 00000000000000..605d1b55c017a5
--- /dev/null
+++ b/webnn/resources/test_data/resample2d.json
@@ -0,0 +1,527 @@
+{
+  "tests": [
+    {
+      "name": "resample2d float32 4D tensor default options",
+      "inputs": {
+        "input": {
+          "shape": [1, 1, 4, 6], // nchw
+          "data": [
+            3.8600528355143604,
+            45.18463077286585,
+            87.67153742917091,
+            98.78210347338205,
+            66.3741434682883,
+            3.4115834948988955,
+            86.14930500548006,
+            95.98133720121507,
+            76.87126314100948,
+            16.52591355131756,
+            65.98782867250333,
+            25.470921564461158,
+            22.56010547750855,
+            92.08479613461083,
+            85.80876634651386,
+            92.6316602716033,
+            29.91620870840146,
+            75.40461275485572,
+            62.063754512670435,
+            1.7712158798243394,
+            99.47231285272224,
+            11.440550135595085,
+            25.39634271166711,
+            67.02175102425608
+          ],
+          "type": "float32"
+        }
+      },
+      "expected": {
+        "shape": [1, 1, 4, 6],
+        "data": [
+          3.8600528240203857,
+          45.18463134765625,
+          87.67153930664062,
+          98.7821044921875,
+          66.3741455078125,
+          3.411583423614502,
+          86.14930725097656,
+          95.98133850097656,
+          76.87126159667969,
+          16.52591323852539,
+          65.98783111572266,
+          25.470922470092773,
+          22.56010627746582,
+          92.08479309082031,
+          85.80876922607422,
+          92.63166046142578,
+          29.916208267211914,
+          75.40460968017578,
+          62.06375503540039,
+          1.7712159156799316,
+          99.4723129272461,
+          11.440549850463867,
+          25.396343231201172,
+          67.0217514038086
+        ],
+        "type": "float32"
+      }
+    },
+    {
+      "name": "resample2d(upsample) float32 4D tensor options.scales",
+      "inputs": {
+        "input": {
+          "shape": [1, 1, 2, 3],
+          "data": [
+            59.92947164849423,
+            41.989187594696546,
+            66.39534663077877,
+            90.70066412516924,
+            86.95106056135486,
+            79.10004839481242
+          ],
+          "type": "float32"
+        }
+      },
+      "options": {
+        "scales": [2.0, 2.0]
+      },
+      "expected": {
+        "shape": [1, 1, 4, 6],
+        "data": [
+          59.92947006225586,
+          59.92947006225586,
+          41.98918914794922,
+          41.98918914794922,
+          66.39534759521484,
+          66.39534759521484,
+          59.92947006225586,
+          59.92947006225586,
+          41.98918914794922,
+          41.98918914794922,
+          66.39534759521484,
+          66.39534759521484,
+          90.7006607055664,
+          90.7006607055664,
+          86.95105743408203,
+          86.95105743408203,
+          79.10005187988281,
+          79.10005187988281,
+          90.7006607055664,
+          90.7006607055664,
+          86.95105743408203,
+          86.95105743408203,
+          79.10005187988281,
+          79.10005187988281
+        ],
+        "type": "float32"
+      }
+    },
+    {
+      "name": "resample2d(upsample) float32 4D tensor options.sizes",
+      "inputs": {
+        "input": {
+          "shape": [1, 1, 2, 3],
+          "data": [
+            59.92947164849423,
+            41.989187594696546,
+            66.39534663077877,
+            90.70066412516924,
+            86.95106056135486,
+            79.10004839481242
+          ],
+          "type": "float32"
+        }
+      },
+      "options": {
+        "sizes": [4, 6]
+      },
+      "expected": {
+        "shape": [1, 1, 4, 6],
+        "data": [
+          59.92947006225586,
+          59.92947006225586,
+          41.98918914794922,
+          41.98918914794922,
+          66.39534759521484,
+          66.39534759521484,
+          59.92947006225586,
+          59.92947006225586,
+          41.98918914794922,
+          41.98918914794922,
+          66.39534759521484,
+          66.39534759521484,
+          90.7006607055664,
+          90.7006607055664,
+          86.95105743408203,
+          86.95105743408203,
+          79.10005187988281,
+          79.10005187988281,
+          90.7006607055664,
+          90.7006607055664,
+          86.95105743408203,
+          86.95105743408203,
+          79.10005187988281,
+          79.10005187988281
+        ],
+        "type": "float32"
+      }
+    },
+    {
+      "name": "resample2d(upsample) float32 4D tensor options.sizes ignored options.scales",
+      "inputs": {
+        "input": {
+          "shape": [1, 1, 2, 3],
+          "data": [
+            59.92947164849423,
+            41.989187594696546,
+            66.39534663077877,
+            90.70066412516924,
+            86.95106056135486,
+            79.10004839481242
+          ],
+          "type": "float32"
+        }
+      },
+      "options": {
+        "scales": [0.5, 0.5],
+        "sizes": [4, 6]
+      },
+      "expected": {
+        "shape": [1, 1, 4, 6],
+        "data": [
+          59.92947006225586,
+          59.92947006225586,
+          41.98918914794922,
+          41.98918914794922,
+          66.39534759521484,
+          66.39534759521484,
+          59.92947006225586,
+          59.92947006225586,
+          41.98918914794922,
+          41.98918914794922,
+          66.39534759521484,
+          66.39534759521484,
+          90.7006607055664,
+          90.7006607055664,
+          86.95105743408203,
+          86.95105743408203,
+          79.10005187988281,
+          79.10005187988281,
+          90.7006607055664,
+          90.7006607055664,
+          86.95105743408203,
+          86.95105743408203,
+          79.10005187988281,
+          79.10005187988281
+        ],
+        "type": "float32"
+      }
+    },
+    {
+      "name": "resample2d(upsample) float32 4D tensor options.axes=[1, 2]",
+      "inputs": {
+        "input": {
+          "shape": [1, 2, 3, 1], // nhwc
+          "data": [
+            59.92947164849423,
+            41.989187594696546,
+            66.39534663077877,
+            90.70066412516924,
+            86.95106056135486,
+            79.10004839481242
+          ],
+          "type": "float32"
+        }
+      },
+      "options": {
+        "sizes": [4, 6],
+        "axes": [1, 2]
+      },
+      "expected": {
+        "shape": [1, 4, 6, 1],
+        "data": [
+          59.92947006225586,
+          59.92947006225586,
+          41.98918914794922,
+          41.98918914794922,
+          66.39534759521484,
+          66.39534759521484,
+          59.92947006225586,
+          59.92947006225586,
+          41.98918914794922,
+          41.98918914794922,
+          66.39534759521484,
+          66.39534759521484,
+          90.7006607055664,
+          90.7006607055664,
+          86.95105743408203,
+          86.95105743408203,
+          79.10005187988281,
+          79.10005187988281,
+          90.7006607055664,
+          90.7006607055664,
+          86.95105743408203,
+          86.95105743408203,
+          79.10005187988281,
+          79.10005187988281
+        ],
+        "type": "float32"
+      }
+    },
+    {
+      "name": "resample2d(upsample) float32 4D tensor explicit options.axes=[2, 3]",
+      "inputs": {
+        "input": {
+          "shape": [1, 1, 2, 3], // nchw
+          "data": [
+            59.92947164849423,
+            41.989187594696546,
+            66.39534663077877,
+            90.70066412516924,
+            86.95106056135486,
+            79.10004839481242
+          ],
+          "type": "float32"
+        }
+      },
+      "options": {
+        "sizes": [4, 6],
+        "axes": [2, 3]
+      },
+      "expected": {
+        "shape": [1, 1, 4, 6],
+        "data": [
+          59.92947006225586,
+          59.92947006225586,
+          41.98918914794922,
+          41.98918914794922,
+          66.39534759521484,
+          66.39534759521484,
+          59.92947006225586,
+          59.92947006225586,
+          41.98918914794922,
+          41.98918914794922,
+          66.39534759521484,
+          66.39534759521484,
+          90.7006607055664,
+          90.7006607055664,
+          86.95105743408203,
+          86.95105743408203,
+          79.10005187988281,
+          79.10005187988281,
+          90.7006607055664,
+          90.7006607055664,
+          86.95105743408203,
+          86.95105743408203,
+          79.10005187988281,
+          79.10005187988281
+        ],
+        "type": "float32"
+      }
+    },
+    {
+      "name": "resample2d(upsample) float32 4D tensor explicit options.mode='nearest-neighbor'",
+      "inputs": {
+        "input": {
+          "shape": [1, 1, 2, 3],
+          "data": [
+            59.92947164849423,
+            41.989187594696546,
+            66.39534663077877,
+            90.70066412516924,
+            86.95106056135486,
+            79.10004839481242
+          ],
+          "type": "float32"
+        }
+      },
+      "options": {
+        "mode": "nearest-neighbor",
+        "sizes": [4, 6]
+      },
+      "expected": {
+        "shape": [1, 1, 4, 6],
+        "data": [
+          59.92947006225586,
+          59.92947006225586,
+          41.98918914794922,
+          41.98918914794922,
+          66.39534759521484,
+          66.39534759521484,
+          59.92947006225586,
+          59.92947006225586,
+          41.98918914794922,
+          41.98918914794922,
+          66.39534759521484,
+          66.39534759521484,
+          90.7006607055664,
+          90.7006607055664,
+          86.95105743408203,
+          86.95105743408203,
+          79.10005187988281,
+          79.10005187988281,
+          90.7006607055664,
+          90.7006607055664,
+          86.95105743408203,
+          86.95105743408203,
+          79.10005187988281,
+          79.10005187988281
+        ],
+        "type": "float32"
+      }
+    },
+    {
+      "name": "resample2d(upsample) float32 4D tensor options.scales options.mode='linear'",
+      "inputs": {
+        "input": {
+          "shape": [1, 1, 2, 3],
+          "data": [
+            59.92947164849423,
+            41.989187594696546,
+            66.39534663077877,
+            90.70066412516924,
+            86.95106056135486,
+            79.10004839481242
+          ],
+          "type": "float32"
+        }
+      },
+      "options": {
+        "mode": "linear",
+        "scales": [2.0, 2.0]
+      },
+      "expected": {
+        "shape": [1, 1, 4, 6],
+        "data": [
+          59.92947006225586,
+          55.444400787353516,
+          46.47425842285156,
+          48.090728759765625,
+          60.29380798339844,
+          66.39534759521484,
+          67.62226867675781,
+          64.02411651611328,
+          56.82780838012695,
+          57.31512451171875,
+          65.48605346679688,
+          69.57152557373047,
+          83.00786590576172,
+          81.18354797363281,
+          77.534912109375,
+          75.76390838623047,
+          75.87055206298828,
+          75.92387390136719,
+          90.7006607055664,
+          89.76325988769531,
+          87.88845825195312,
+          84.9883041381836,
+          81.06280517578125,
+          79.10005187988281
+        ],
+        "type": "float32"
+      }
+    },
+    {
+      "name": "resample2d(upsample) float32 4D tensor options.sizes options.mode='linear'",
+      "inputs": {
+        "input": {
+          "shape": [1, 1, 2, 3],
+          "data": [
+            59.92947164849423,
+            41.989187594696546,
+            66.39534663077877,
+            90.70066412516924,
+            86.95106056135486,
+            79.10004839481242
+          ],
+          "type": "float32"
+        }
+      },
+      "options": {
+        "mode": "linear",
+        "sizes": [4, 6]
+      },
+      "expected": {
+        "shape": [1, 1, 4, 6],
+        "data": [
+          59.92947006225586,
+          55.444400787353516,
+          46.47425842285156,
+          48.090728759765625,
+          60.29380798339844,
+          66.39534759521484,
+          67.62226867675781,
+          64.02411651611328,
+          56.82780838012695,
+          57.31512451171875,
+          65.48605346679688,
+          69.57152557373047,
+          83.00786590576172,
+          81.18354797363281,
+          77.534912109375,
+          75.76390838623047,
+          75.87055206298828,
+          75.92387390136719,
+          90.7006607055664,
+          89.76325988769531,
+          87.88845825195312,
+          84.9883041381836,
+          81.06280517578125,
+          79.10005187988281
+        ],
+        "type": "float32"
+      }
+    },
+    {
+      "name": "resample2d(upsample) float32 4D tensor options.axes=[1, 2] options.mode='linear'",
+      "inputs": {
+        "input": {
+          "shape": [1, 2, 3, 1],
+          "data": [
+            59.92947164849423,
+            41.989187594696546,
+            66.39534663077877,
+            90.70066412516924,
+            86.95106056135486,
+            79.10004839481242
+          ],
+          "type": "float32"
+        }
+      },
+      "options": {
+        "mode": "linear",
+        "sizes": [4, 6],
+        "axes": [1, 2]
+      },
+      "expected": {
+        "shape": [1, 4, 6, 1],
+        "data": [
+          59.92947006225586,
+          55.444400787353516,
+          46.47425842285156,
+          48.090728759765625,
+          60.29380798339844,
+          66.39534759521484,
+          67.62226867675781,
+          64.02411651611328,
+          56.82780838012695,
+          57.31512451171875,
+          65.48605346679688,
+          69.57152557373047,
+          83.00786590576172,
+          81.18354797363281,
+          77.534912109375,
+          75.76390838623047,
+          75.87055206298828,
+          75.92387390136719,
+          90.7006607055664,
+          89.76325988769531,
+          87.88845825195312,
+          84.9883041381836,
+          81.06280517578125,
+          79.10005187988281
+        ],
+        "type": "float32"
+      }
+    }
+  ]
+}
\ No newline at end of file
diff --git a/webnn/resources/utils.js b/webnn/resources/utils.js
index 0e404dbcd07e4b..375c71174a8d11 100644
--- a/webnn/resources/utils.js
+++ b/webnn/resources/utils.js
@@ -282,6 +282,32 @@ const getReductionPrecisionTolerance = (resources, operationName) => {
   return tolerance;
 };
 
+/**
+ * Get ULP tolerance of resample2d operations.
+ * @param {Object} resources - Resources used for building a graph
+ * @param {String} operationName - An operation name
+ * @returns {Number} A tolerance number
+ */
+const getResample2dPrecisionTolerance = (resources, operationName) => {
+  const options = {...resources.options};
+  let tolerance;
+  if (options.mode && options.mode === 'linear') {
+    // interpolation mode is linear
+    const precisionType = resources.expected.type;
+    if (precisionType === 'float32') {
+      tolerance = 84;
+    } else if (precisionType === 'float16') {
+      tolerance = 10;
+    } else {
+      tolerance = 1;
+    }
+  } else {
+    // interpolation mode is nearest-neighbor
+    tolerance = 0;
+  }
+  return tolerance;
+};
+
 // Refer to precision metrics on https://github.com/webmachinelearning/webnn/issues/265#issuecomment-1256242643
 const PrecisionMetrics = {
   argMax: {ULP: {int64: 0}},
@@ -356,6 +382,7 @@ const PrecisionMetrics = {
   reduceSumSquare: {ULP: {float32: getReductionPrecisionTolerance, float16: getReductionPrecisionTolerance}},
   // End Reduction operations
   relu: {ULP: {float32: 0, float16: 0}},
+  resample2d: {ULP: {float32: getResample2dPrecisionTolerance, float16: getResample2dPrecisionTolerance}},
   reshape: {ULP: {float32: 0, float16: 0}},
   sigmoid: {ULP: {float32: 32+2, float16: 3}}, // float32 (leaving a few ULP for roundoff)
   slice: {ULP: {float32: 0, float16: 0}},