Climate change evidence has been documented by different authors based on the long years’ monthly temperature measurements since long back. In the literature, annual-mean and 5-year moving average time series of global mean land–ocean temperature index and meteorological station data of global annual-mean surface air temperature changes are presented with a base period including some parts as uncertainty estimates. This paper provides an innovative method for refined calculation of global warming calculation.

Climate change impact search methodologies, for a single hydro-meteorology record, are based mostly on the probabilistic, statistical and stochastic processes. The applications of these methodologies require some restrictive assumptions for their validation, which may not be embedded in the structure of the record. Either transformations or approximations are taken into consideration with idealization and simplification assumptions. In this paper, a series of conceptual trend polygon methodologies are presented without mathematical expression and restrictive assumptions. These are serial, cross and double polygonal trend methods, in addition to trend star graphs. They help to identify, interpret and calculate the monthly shifts leading to useful scientific information, which cannot be obtained by the classical monotonic trend identification approaches. The application of the methodology is presented for hydro-meteorological series from different regions of the world including precipitation and discharge records.