diff --git a/packages/@aws-cdk/aws-cloudwatch/lib/metric-types.ts b/packages/@aws-cdk/aws-cloudwatch/lib/metric-types.ts
index e44ce2bb7c66e..f77b95f57430b 100644
--- a/packages/@aws-cdk/aws-cloudwatch/lib/metric-types.ts
+++ b/packages/@aws-cdk/aws-cloudwatch/lib/metric-types.ts
@@ -37,7 +37,6 @@ export interface IMetric {
  * Metric dimension
  *
  * @see https://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-cw-dimension.html
- *
  */
 export interface Dimension {
   /**
@@ -53,6 +52,8 @@ export interface Dimension {
 
 /**
  * Statistic to use over the aggregation period
+ *
+ * @see https://docs.aws.amazon.com/AmazonCloudWatch/latest/monitoring/Statistics-definitions.html
  */
 export enum Statistic {
   /**
@@ -78,7 +79,382 @@ export enum Statistic {
    * The highest value observed during the specified period.
    * You can use this value to determine high volumes of activity for your application.
    */
-  MAXIMUM = 'Maximum'
+  MAXIMUM = 'Maximum',
+
+  /**
+   * Percentile (p) indicates the relative standing of a value in a dataset.
+   * Percentiles help you get a better understanding of the distribution of your metric data.
+   *
+   * p10 is the 10th percentile and means that 10% of the data within the period is lower than this value and 90% of the data is higher than this value.
+   */
+  P10 = 'p10',
+  /**
+   * Percentile (p) indicates the relative standing of a value in a dataset.
+   * Percentiles help you get a better understanding of the distribution of your metric data.
+   *
+   * p50 is the 50th percentile and means that 50% of the data within the period is lower than this value and 50% of the data is higher than this value.
+   */
+  P50 = 'p50',
+  /**
+   * Percentile (p) indicates the relative standing of a value in a dataset.
+   * Percentiles help you get a better understanding of the distribution of your metric data.
+   *
+   * p90 is the 90th percentile and means that 90% of the data within the period is lower than this value and 10% of the data is higher than this value.
+   */
+  P90 = 'p90',
+  /**
+   * Percentile (p) indicates the relative standing of a value in a dataset.
+   * Percentiles help you get a better understanding of the distribution of your metric data.
+   *
+   * p95 is the 95th percentile and means that 95% of the data within the period is lower than this value and 5% of the data is higher than this value.
+   */
+  P95 = 'p95',
+  /**
+   * Percentile (p) indicates the relative standing of a value in a dataset.
+   * Percentiles help you get a better understanding of the distribution of your metric data.
+   *
+   * p99 is the 99th percentile and means that 99% of the data within the period is lower than this value and 1% of the data is higher than this value.
+   */
+  P99 = 'p99',
+  /**
+   * Percentile (p) indicates the relative standing of a value in a dataset.
+   * Percentiles help you get a better understanding of the distribution of your metric data.
+   *
+   * p99.9 is the 99.9th percentile and means that 99.9% of the data within the period is lower than this value and 0.1% of the data is higher than this value.
+   */
+  P99_9 = 'p99.9',
+  /**
+   * Percentile (p) indicates the relative standing of a value in a dataset.
+   * Percentiles help you get a better understanding of the distribution of your metric data.
+   *
+   * p99.99 is the 99.99th percentile and means that 99.9% of the data within the period is lower than this value and 0.01% of the data is higher than this value.
+   */
+  P99_99 = 'p99.99',
+
+  /**
+   * Trimmed mean (TM) is the mean of all values that are between two specified boundaries. Values outside of the boundaries are ignored when the mean is calculated.
+   * You define the boundaries as one or two numbers between 0 and 100, up to 10 decimal places. The numbers can be absolute values or percentages.
+   *
+   * tm10 calculates the average after removing the 90% of data points with the highest values.
+   */
+  TM10 = 'tm10',
+  /**
+   * Trimmed mean (TM) is the mean of all values that are between two specified boundaries. Values outside of the boundaries are ignored when the mean is calculated.
+   * You define the boundaries as one or two numbers between 0 and 100, up to 10 decimal places. The numbers can be absolute values or percentages.
+   *
+   * tm50 calculates the average after removing the 50% of data points with the highest values.
+   */
+  TM50 = 'tm50',
+  /**
+   * Trimmed mean (TM) is the mean of all values that are between two specified boundaries. Values outside of the boundaries are ignored when the mean is calculated.
+   * You define the boundaries as one or two numbers between 0 and 100, up to 10 decimal places. The numbers can be absolute values or percentages.
+   *
+   * tm90 calculates the average after removing the 10% of data points with the highest values.
+   */
+  TM90 = 'tm90',
+  /**
+   * Trimmed mean (TM) is the mean of all values that are between two specified boundaries. Values outside of the boundaries are ignored when the mean is calculated.
+   * You define the boundaries as one or two numbers between 0 and 100, up to 10 decimal places. The numbers can be absolute values or percentages.
+   *
+   * tm95 calculates the average after removing the 5% of data points with the highest values.
+   */
+  TM95 = 'tm95',
+  /**
+   * Trimmed mean (TM) is the mean of all values that are between two specified boundaries. Values outside of the boundaries are ignored when the mean is calculated.
+   * You define the boundaries as one or two numbers between 0 and 100, up to 10 decimal places. The numbers can be absolute values or percentages.
+   *
+   * tm99 calculates the average after removing the 1% of data points with the highest values.
+   */
+  TM99 = 'tm99',
+  /**
+   * Trimmed mean (TM) is the mean of all values that are between two specified boundaries. Values outside of the boundaries are ignored when the mean is calculated.
+   * You define the boundaries as one or two numbers between 0 and 100, up to 10 decimal places. The numbers can be absolute values or percentages.
+   *
+   * tm99.9 calculates the average after removing the 0.1% of data points with the highest values.
+   */
+  TM99_9 = 'tm99.9',
+  /**
+   * Trimmed mean (TM) is the mean of all values that are between two specified boundaries. Values outside of the boundaries are ignored when the mean is calculated.
+   * You define the boundaries as one or two numbers between 0 and 100, up to 10 decimal places. The numbers can be absolute values or percentages.
+   *
+   * tm99 calculates the average after removing the 0.01% of data points with the highest values.
+   */
+  TM99_99 = 'tm99.99',
+
+  /**
+   * Trimmed mean (TM) is the mean of all values that are between two specified boundaries. Values outside of the boundaries are ignored when the mean is calculated.
+   * You define the boundaries as one or two numbers between 0 and 100, up to 10 decimal places. The numbers can be absolute values or percentages.
+   *
+   * TM(1%:99%) calculates the average after removing the 1% lowest data points and the 1% highest data points.
+   */
+  TM_1P_99P = 'TM(1%:99%)',
+  /**
+   * Trimmed mean (TM) is the mean of all values that are between two specified boundaries. Values outside of the boundaries are ignored when the mean is calculated.
+   * You define the boundaries as one or two numbers between 0 and 100, up to 10 decimal places. The numbers can be absolute values or percentages.
+   *
+   * TM(2%:98%) calculates the average after removing the 2% lowest data points and the 2% highest data points.
+   */
+  TM_2P_98P = 'TM(2%:98%)',
+  /**
+   * Trimmed mean (TM) is the mean of all values that are between two specified boundaries. Values outside of the boundaries are ignored when the mean is calculated.
+   * You define the boundaries as one or two numbers between 0 and 100, up to 10 decimal places. The numbers can be absolute values or percentages.
+   *
+   * TM(5%:95%) calculates the average after removing the 5% lowest data points and the 5% highest data points.
+   */
+  TM_5P_95P = 'TM(5%:95%)',
+  /**
+   * Trimmed mean (TM) is the mean of all values that are between two specified boundaries. Values outside of the boundaries are ignored when the mean is calculated.
+   * You define the boundaries as one or two numbers between 0 and 100, up to 10 decimal places. The numbers can be absolute values or percentages.
+   *
+   * TM(10%:90%) calculates the average after removing the 10% lowest data points and the 10% highest data points.
+   */
+  TM_10P_90P = 'TM(10%:90%)',
+
+  /**
+   * Interquartile mean (IQM) is the trimmed mean of the interquartile range, or the middle 50% of values. It is equivalent to TM(25%:75%).
+   */
+  IQM = 'IQM',
+
+  /**
+   * Winsorized mean (WM) is similar to trimmed mean. However, with winsorized mean, the values that are outside the boundary are not ignored,
+   * but instead are considered to be equal to the value at the edge of the appropriate boundary.
+   * After this normalization, the average is calculated. You define the boundaries as one or two numbers between 0 and 100, up to 10 decimal places.
+   *
+   * wm10 calculates the average while treating the 90% of the highest values to be equal to the value at the 10th percentile.
+   */
+  WM10 = 'wm10',
+  /**
+   * Winsorized mean (WM) is similar to trimmed mean. However, with winsorized mean, the values that are outside the boundary are not ignored,
+   * but instead are considered to be equal to the value at the edge of the appropriate boundary.
+   * After this normalization, the average is calculated. You define the boundaries as one or two numbers between 0 and 100, up to 10 decimal places.
+   *
+   * wm50 calculates the average while treating the 50% of the highest values to be equal to the value at the 50th percentile.
+   */
+  WM50 = 'wm50',
+  /**
+   * Winsorized mean (WM) is similar to trimmed mean. However, with winsorized mean, the values that are outside the boundary are not ignored,
+   * but instead are considered to be equal to the value at the edge of the appropriate boundary.
+   * After this normalization, the average is calculated. You define the boundaries as one or two numbers between 0 and 100, up to 10 decimal places.
+   *
+   * wm90 calculates the average while treating the 10% of the highest values to be equal to the value at the 90th percentile.
+   */
+  WM90 = 'wm90',
+  /**
+   * Winsorized mean (WM) is similar to trimmed mean. However, with winsorized mean, the values that are outside the boundary are not ignored,
+   * but instead are considered to be equal to the value at the edge of the appropriate boundary.
+   * After this normalization, the average is calculated. You define the boundaries as one or two numbers between 0 and 100, up to 10 decimal places.
+   *
+   * wm95 calculates the average while treating the 5% of the highest values to be equal to the value at the 95th percentile.
+   */
+  WM95 = 'wm95',
+  /**
+   * Winsorized mean (WM) is similar to trimmed mean. However, with winsorized mean, the values that are outside the boundary are not ignored,
+   * but instead are considered to be equal to the value at the edge of the appropriate boundary.
+   * After this normalization, the average is calculated. You define the boundaries as one or two numbers between 0 and 100, up to 10 decimal places.
+   *
+   * wm99 calculates the average while treating the 1% of the highest values to be equal to the value at the 99th percentile.
+   */
+  WM99 = 'wm99',
+  /**
+   * Winsorized mean (WM) is similar to trimmed mean. However, with winsorized mean, the values that are outside the boundary are not ignored,
+   * but instead are considered to be equal to the value at the edge of the appropriate boundary.
+   * After this normalization, the average is calculated. You define the boundaries as one or two numbers between 0 and 100, up to 10 decimal places.
+   *
+   * wm99.9 calculates the average while treating the 0.1% of the highest values to be equal to the value at the 99.9th percentile.
+   */
+  WM99_9 = 'wm99.9',
+  /**
+   * Winsorized mean (WM) is similar to trimmed mean. However, with winsorized mean, the values that are outside the boundary are not ignored,
+   * but instead are considered to be equal to the value at the edge of the appropriate boundary.
+   * After this normalization, the average is calculated. You define the boundaries as one or two numbers between 0 and 100, up to 10 decimal places.
+   *
+   * wm99.99 calculates the average while treating the 0.01% of the highest values to be equal to the value at the 99.99th percentile.
+   */
+  WM99_99 = 'wm99.99',
+
+  /**
+   * Winsorized mean (WM) is similar to trimmed mean. However, with winsorized mean, the values that are outside the boundary are not ignored,
+   * but instead are considered to be equal to the value at the edge of the appropriate boundary.
+   * After this normalization, the average is calculated. You define the boundaries as one or two numbers between 0 and 100, up to 10 decimal places.
+   *
+   * WM(1%:99%) calculates the average while treating the highest 1% of data points to be the value of the 99% boundary,
+   * and treating the lowest 1% of data points to be the value of the 1% boundary.
+   */
+  WM_1P_99P = 'WM(1%:99%)',
+  /**
+   * Winsorized mean (WM) is similar to trimmed mean. However, with winsorized mean, the values that are outside the boundary are not ignored,
+   * but instead are considered to be equal to the value at the edge of the appropriate boundary.
+   * After this normalization, the average is calculated. You define the boundaries as one or two numbers between 0 and 100, up to 10 decimal places.
+   *
+   * WM(2%:98%) calculates the average while treating the highest 2% of data points to be the value of the 98% boundary,
+   * and treating the lowest 2% of data points to be the value of the 2% boundary.
+   */
+  WM_2P_98P = 'WM(2%:98%)',
+  /**
+   * Winsorized mean (WM) is similar to trimmed mean. However, with winsorized mean, the values that are outside the boundary are not ignored,
+   * but instead are considered to be equal to the value at the edge of the appropriate boundary.
+   * After this normalization, the average is calculated. You define the boundaries as one or two numbers between 0 and 100, up to 10 decimal places.
+   *
+   * WM(5%:95%) calculates the average while treating the highest 5% of data points to be the value of the 95% boundary,
+   * and treating the lowest 5% of data points to be the value of the 5% boundary.
+   */
+  WM_5P_95P = 'WM(5%:95%)',
+  /**
+   * Winsorized mean (WM) is similar to trimmed mean. However, with winsorized mean, the values that are outside the boundary are not ignored,
+   * but instead are considered to be equal to the value at the edge of the appropriate boundary.
+   * After this normalization, the average is calculated. You define the boundaries as one or two numbers between 0 and 100, up to 10 decimal places.
+   *
+   * WM(10%:90%) calculates the average while treating the highest 10% of data points to be the value of the 90% boundary,
+   * and treating the lowest 10% of data points to be the value of the 10% boundary.
+   */
+  WM_10P_90P = 'WM(10%:90%)',
+
+  /**
+   * Trimmed count (TC) is the number of data points in the chosen range for a trimmed mean statistic.
+   *
+   * tc10 returns the number of data points not including any data points that fall in the highest 90% of the values.
+   */
+  TC10 = 'tc10',
+  /**
+   * Trimmed count (TC) is the number of data points in the chosen range for a trimmed mean statistic.
+   *
+   * tc50 returns the number of data points not including any data points that fall in the highest 50% of the values.
+   */
+  TC50 = 'tc50',
+  /**
+   * Trimmed count (TC) is the number of data points in the chosen range for a trimmed mean statistic.
+   *
+   * tc90 returns the number of data points not including any data points that fall in the highest 10% of the values.
+   */
+  TC90 = 'tc90',
+  /**
+   * Trimmed count (TC) is the number of data points in the chosen range for a trimmed mean statistic.
+   *
+   * tc95 returns the number of data points not including any data points that fall in the highest 5% of the values.
+   */
+  TC95 = 'tc95',
+  /**
+   * Trimmed count (TC) is the number of data points in the chosen range for a trimmed mean statistic.
+   *
+   * tc99 returns the number of data points not including any data points that fall in the highest 1% of the values.
+   */
+  TC99 = 'tc99',
+  /**
+   * Trimmed count (TC) is the number of data points in the chosen range for a trimmed mean statistic.
+   *
+   * tc99.9 returns the number of data points not including any data points that fall in the highest 0.1% of the values.
+   */
+  TC99_9 = 'tc99.9',
+  /**
+   * Trimmed count (TC) is the number of data points in the chosen range for a trimmed mean statistic.
+   *
+   * tc99.99 returns the number of data points not including any data points that fall in the highest 0.01% of the values.
+   */
+  TC99_99 = 'tc99.99',
+
+  /**
+   * Trimmed count (TC) is the number of data points in the chosen range for a trimmed mean statistic.
+   *
+   * TC(1%:99%) returns the number of data points not including any data points that fall in the lowest 1% of the values and the highest 99% of the values.
+   */
+  TC_1P_99P = 'TC(1%:99%)',
+  /**
+   * Trimmed count (TC) is the number of data points in the chosen range for a trimmed mean statistic.
+   *
+   * TC(2%:98%) returns the number of data points not including any data points that fall in the lowest 2% of the values and the highest 98% of the values.
+   */
+  TC_2P_98P = 'TC(2%:98%)',
+  /**
+   * Trimmed count (TC) is the number of data points in the chosen range for a trimmed mean statistic.
+   *
+   * TC(5%:95%) returns the number of data points not including any data points that fall in the lowest 5% of the values and the highest 95% of the values.
+   */
+  TC_5P_95P = 'TC(5%:95%)',
+  /**
+   * Trimmed count (TC) is the number of data points in the chosen range for a trimmed mean statistic.
+   *
+   * TC(10%:90%) returns the number of data points not including any data points that fall in the lowest 10% of the values and the highest 90% of the values.
+   */
+  TC_10P_90P = 'TC(10%:90%)',
+
+  /**
+   * Trimmed sum (TS) is the sum of the values of data points in a chosen range for a trimmed mean statistic.
+   * It is equivalent to `(Trimmed Mean) * (Trimmed count)`.
+   *
+   * ts10 returns the sum of the data points not including any data points that fall in the highest 90% of the values.
+   */
+  TS10 = 'ts10',
+  /**
+   * Trimmed sum (TS) is the sum of the values of data points in a chosen range for a trimmed mean statistic.
+   * It is equivalent to `(Trimmed Mean) * (Trimmed count)`.
+   *
+   * ts50 returns the sum of the data points not including any data points that fall in the highest 50% of the values.
+   */
+  TS50 = 'ts50',
+  /**
+   * Trimmed sum (TS) is the sum of the values of data points in a chosen range for a trimmed mean statistic.
+   * It is equivalent to `(Trimmed Mean) * (Trimmed count)`.
+   *
+   * ts90 returns the sum of the data points not including any data points that fall in the highest 10% of the values.
+   */
+  TS90 = 'ts90',
+  /**
+   * Trimmed sum (TS) is the sum of the values of data points in a chosen range for a trimmed mean statistic.
+   * It is equivalent to `(Trimmed Mean) * (Trimmed count)`.
+   *
+   * ts95 returns the sum of the data points not including any data points that fall in the highest 5% of the values.
+   */
+  TS95 = 'ts95',
+  /**
+   * Trimmed sum (TS) is the sum of the values of data points in a chosen range for a trimmed mean statistic.
+   * It is equivalent to `(Trimmed Mean) * (Trimmed count)`.
+   *
+   * ts99 returns the sum of the data points not including any data points that fall in the highest 1% of the values.
+   */
+  TS99 = 'ts99',
+  /**
+   * Trimmed sum (TS) is the sum of the values of data points in a chosen range for a trimmed mean statistic.
+   * It is equivalent to `(Trimmed Mean) * (Trimmed count)`.
+   *
+   * ts99.9 returns the sum of the data points not including any data points that fall in the highest 0.1% of the values.
+   */
+  TS99_9 = 'ts99.9',
+  /**
+   * Trimmed sum (TS) is the sum of the values of data points in a chosen range for a trimmed mean statistic.
+   * It is equivalent to `(Trimmed Mean) * (Trimmed count)`.
+   *
+   * ts99.99 returns the sum of the data points not including any data points that fall in the highest 0.01% of the values.
+   */
+  TS99_99 = 'ts99.99',
+
+  /**
+   * Trimmed sum (TS) is the sum of the values of data points in a chosen range for a trimmed mean statistic.
+   * It is equivalent to `(Trimmed Mean) * (Trimmed count)`.
+   *
+   * TS(1%:99%) returns the sum of the data points not including any data points that fall in the lowest 1% of the values and the highest 99% of the values.
+   */
+  TS_1P_99P = 'TS(1%:99%)',
+  /**
+   * Trimmed sum (TS) is the sum of the values of data points in a chosen range for a trimmed mean statistic.
+   * It is equivalent to `(Trimmed Mean) * (Trimmed count)`.
+   *
+   * TS(2%:98%) returns the sum of the data points not including any data points that fall in the lowest 2% of the values and the highest 98% of the values.
+   */
+  TS_2P_98P = 'TS(2%:98%)',
+  /**
+   * Trimmed sum (TS) is the sum of the values of data points in a chosen range for a trimmed mean statistic.
+   * It is equivalent to `(Trimmed Mean) * (Trimmed count)`.
+   *
+   * TS(5%:95%) returns the sum of the data points not including any data points that fall in the lowest 5% of the values and the highest 95% of the values.
+   */
+  TS_5P_95P = 'TS(5%:95%)',
+  /**
+   * Trimmed sum (TS) is the sum of the values of data points in a chosen range for a trimmed mean statistic.
+   * It is equivalent to `(Trimmed Mean) * (Trimmed count)`.
+   *
+   * TS(10%:90%) returns the sum of the data points not including any data points that fall in the lowest 10% of the values and the highest 90% of the values.
+   */
+  TS_10P_90P = 'TS(10%:90%)',
 }
 
 /**
@@ -218,7 +594,7 @@ export enum Unit {
   /**
    * None
    */
-  NONE = 'None'
+  NONE = 'None',
 }
 
 /**
diff --git a/packages/@aws-cdk/aws-cloudwatch/lib/metric.ts b/packages/@aws-cdk/aws-cloudwatch/lib/metric.ts
index 5d7955dc686cb..06ecae7ca8edc 100644
--- a/packages/@aws-cdk/aws-cloudwatch/lib/metric.ts
+++ b/packages/@aws-cdk/aws-cloudwatch/lib/metric.ts
@@ -32,6 +32,11 @@ export interface CommonMetricOptions {
    * - "Sum" | "sum"
    * - "SampleCount | "n"
    * - "pNN.NN"
+   * - "tmNN.NN" | "tm(NN.NN%:NN.NN%)"
+   * - "iqm"
+   * - "wmNN.NN" | "wm(NN.NN%:NN.NN%)"
+   * - "tcNN.NN" | "tc(NN.NN%:NN.NN%)"
+   * - "tsNN.NN" | "ts(NN.NN%:NN.NN%)"
    *
    * @default Average
    */