Assessment of Reanalysis Daily Extreme Temperatures with China''s Homogenized Historical Dataset during 1979--2001 Using Probability Density Functions.

Using a recently homogenized observational daily maximum ( T_MAX) and minimum temperature ( T_MIN) dataset for China, the extreme temperatures from the 40-yr ECMWF Re-Analysis (ERA-40), the Japanese 25-year Reanalysis (JRA-25), the NCEP/Department of Energy Global Reanalysis 2 (NCEP-2), and the ECMWF''s ERA-Interim (ERAIn) reanalyses for summer (June--August) and winter (December--February) are assessed by probability density functions for the periods 1979--2001 and 1990--2001. For 1979--2001, no single reanalysis appears to be consistently accurate across eight areas examined over China. The ERA-40 and JRA-25 reanalyses show similar representations and close skill scores over most of the regions of China for both seasons. NCEP-2 generally has lower skill scores, especially over regions with complex topography. The regional and seasonal differences identified are commonly associated with different geographical locations and the methods used to diagnose these quantities. All the selected reanalysis products exhibit better performance for winter compared to summer over most regions of China. The T_MAX values from the reanalysis tend to be systematically underestimated, while T_MIN is systematically closer to observed values than T_MAX. Comparisons of the reanalyses to reproduce the 99.7 percentiles for T_MAX and 0.3 percentiles for T_MIN show that most reanalyses tend to underestimate the 99.7 percentiles in maximum temperature both in summer and winter. For the 0.3 percentiles in T_MIN, NCEP-2 is relatively inaccurate with a −−12°°C cold bias over the Qinghai--Tibetan Plateau in winter. ERA-40 and JRA-25 generally overestimate the extreme T_MIN, and the extreme percentage differences of ERA-40 and JRA-25 are quite similar over all of the regions. The results are generally similar for 1990--2001, but in contrast to the other three reanalysis products the newly released ERAIn is very reasonable, especially for wintertime T_MIN, with a skill score greater than 0.83 for each region of China. This demonstrates the great potential of this product for use in future impact assessments on continental scales where those impacts are based on extreme temperatures. [ABSTRACT FROM AUTHOR]