Extreme wet bulb temperatures are important for a number of applications including the proper and efficient design of building cooling systems. Since wet bulb temperature is not directly available from climate model output and design specifications require information at hourly resolution, whereas twice-daily resolution is more typical of climate models, the ability of climate models to replicate the observed climatology is evaluated at a set of US stations. Observed wet bulb extremes can be replicated by applying a spline fit to the twice-daily humidity and temperature observations that simulate the data available from climate models and then minimizing the residual of the equation specifying the change in enthalpy of moist air. On average, these ersatz values are 1 °C colder than the observed values. Climate model simulations for the period 1950–2005 also generally agree with the ersatz observations. At most locations, the model bias is negative (model values colder than the simulated observations) and on average near 1 °C. The largest positive biases occur at the most arid stations and the largest negative biases are found at the coldest locations. Model projections for the mid-twenty-first century indicate that the most extreme wet bulb temperatures will increase by between 1 and 2.3 °C, with the largest increases at the most northern locations. Future warming and wetting appear to result in a translation of the entire wet bulb cumulative distribution function, leading to similar increases regardless of wet bulb temperature. The increase is fairly consistent among different climate models and at each station.

极端的湿球温度对于许多应用都很重要，包括正确有效地设计建筑物的冷却系统。由于不能直接从气候模型输出中获得湿球温度，并且设计规范要求以小时分辨率提供信息，而每天两次分辨率是气候模型的典型特征，因此在美国一组评估了气候模型复制观测到的气候的能力站。可以通过对每天两次的湿度和温度观测值进行样条拟合来复制观察到的湿球极端值，该样条曲线拟合可模拟气候模型提供的数据，然后最小化指定湿空气焓变的方程式的残差。平均而言，这些eratz值比观测值低1°C。1950-2005年期间的气候模型模拟也普遍与ersatz观测结果一致。在大多数位置，模型偏差为负（模型值比模拟观测值低），平均接近1°C。最大的正偏差出现在最干旱的站点，最大的负偏差出现在最冷的位置。二十一世纪中叶的模型预测表明，最极端的湿球温度将上升1到2.3°C，在最北部的位置上升幅度最大。未来的变暖和润湿似乎会导致整个湿球累积分布函数的转换，无论湿球温度如何，都会导致类似的增加。在不同的气候模式之间以及每个站点，这种增加是相当一致的。