Understanding the dynamics and physics of climate extremes will be a critical challenge for 21st century climate science. Increasing temperatures and saturation vapor pressures may exacerbate heat waves, droughts and precipitation extremes. Yet our ability to monitor temperature variations is limited and declining. Between 1983 and 2016 the number of observations in the CRU Tmax product declined precipitously (5,900→1,000); 1,000 poorly distributed measurements are not enough to resolve regional Tmax variations. Here we show that long (1983-near present) high resolution (0.05°), cloud-screened archives of geostationary satellite Thermal Infrared Red (TIR) observations, when combined with a dense set of ~15,000 station observations, can explain 23, 40, 30, 11% more variance than the CRU over the globe, South America, Africa, India and areas north of 50°N, with even greater levels of improvement over the 2011-2016 period (28, 45, 39, 52, 28%).
Described here for the first time, the TIR Tmax algorithm uses sub-daily TIR distributions to screen out cloud contaminated observations, providing accurate (correlation≈0.8) gridded emission Tmax estimates. Blending these gridded fields with ~15,000 station observations provides a seamless, high-resolution source of accurate Tmax estimates that performs well in areas lacking dense in situ observations and even better where in situ observations are available. Cross-validation results indicates that the satellite-only, station-only and combined products all perform accurately (R≈0.8-0.9, mean absolute errors ≈0.8-1.0). Hence, the Climate Hazards center InfraRed Temperature with Stations (CHIRTSmax) data set should provide a valuable resource for climate change studies, climate extreme analyses, and early warning applications.