### 1. Introduction

_{2}and scenario of Modern_PredictedSST (Sea Surface Temperature) was used to forecasting temperature changes in Pakistan. Doubled_ CO

_{2}model predicted overall 11.38% lower values of temperature than real ones [10]. Global Climate Model (GCM) has been applied to forecasting changes in temperature of Saudi Arabia for two scenarios namely a double carbon dioxide (2CO

_{2}) and a Modern_Predicted SST (Sea Surface Temperature) scenario. The overall change in land surface temperature is a 4.72°C increase by the end of the 21st century [11].

### 2. Material and Methods

### 2.1. Study Area

### 2.2. Data Collection

_{2}simulation) over a period of 53 years (1st January 1968-31st December 2020). This model “measures” the sensitivity of the climate to a doubling of CO

_{2}. Starting with a climate of 1958 and with CO

_{2}concentrations of 314.9 ppm, this produces an instantaneous doubling of CO

_{2}levels to 629.8 ppm. The model then reacts to the forward-looking extra radiative forcing to 2100. So, Doubled_ CO

_{2}means to run equilibrium simulations with an instantaneous doubling of carbon dioxide [32]. Transform software was used for downscaling. The observed data (1st January 1968-31st December 2017) were collected from BMD (Bangladesh Meteorological Department). A continuous data set is a quantitative data set that can have values that are represented as values or fractions. Temperature is a continuous variable and due to the representation as degree Celsius, it can be termed as time interval data. The working process that has been carried out in this work can be shown in the flow diagram (Fig. S1).

### 2.3. EdGCM

### 2.4. Downscaling by Transform Software

### 2.5. Percent of Bias (PBIAS)

##### (1)

$$\text{PBIAS}(\%)=\left[\frac{{\sum}_{i=1}^{n}\left({Y}_{i}^{obs}-{Y}_{i}^{sim}\right)*100}{{\sum}_{i=1}^{n}\left({Y}_{i}^{obs}\right)}\right]$$### 2.6. Nash-Sutcliffe Efficiency (NSE)

### 2.7. Trend Analysis

#### 2.7.1. Mann- Kendall trend test

##### (3)

$$\begin{array}{c}S={\sum}_{i=1}^{n-1}{\sum}_{j=i+1}^{n}sign{T}_{j}-{T}_{i}\\ Sign,{T}_{j}-{T}_{i}=1\hspace{0.17em}if\hspace{0.17em}{T}_{j}-{T}_{i}>0,\hfill \\ 0\hspace{0.17em}if\hspace{0.17em}{T}_{j}-{T}_{i}=0\hspace{0.17em}and\hspace{0.17em}-1\hspace{0.17em}if\hspace{0.17em}{T}_{j}-{T}_{i}<0\end{array}$$*T*

*–*

_{j}*T*

*are the annual values in years*

_{i}*j*and

*i, j>i*, respectively. A positive (negative) value of

*S*shows an upward (downward) trend.

### 3. Results and Discussion

### 3.1. Analysis of EdGCM Model for Doubled_ CO_{2} and Global_ Warming_ 01

_{2}

_{2}simulation for analysis of EdGCM model is shown in Fig. 2. This is the most significant characteristic of a climate model which referred to as “post-processing”. For Doubled_ CO

_{2}simulation, the run started from December 01, 1967 (one month earlier than actual time January 01, 1968) and ended on December 31, 2020. It is standard practice for the simulations to begin one month before the production of analyzable data begins. This is referred to as a period of “spin-up”, during which atmospheric numerical noise subsides. This noise is linked to the fact that there is no perfect balance between the initial conditions and boundary conditions at the beginning. The noise is “ironed out” within a month and significant output starts to accumulate [37].

_{2}run for EdGCM, the global annual average decreases in snow and ice coverage was −4.15%, indicating an increase in surface air temperature of 0.044°C. In some grid cells, the snow and ice cover decrease to −96.6% in the EdGCM runs where local temperature response are maximum likewise much greater than global averages. The certainty or uncertainty associated with the parameterization of sea ice in EdGCM is currently unclear, and from other hydrological cycle variables it is difficult to extract snow and ice feedback [32]. Temperature difference shows how average global temperature has changed from 2010–2100. The average overall increase was 0.32 degrees with a max of 3.32 degrees and a minimum of −1.85 degrees [38].

_{2}trend involves an exponential change of 1% per year from 2000 to 2100. The total amount of global warming at the end of this transient simulation is 4°C and CO

_{2}concentration will be 900 ppm. From IPCC report a slowdown in emissions growth until late in the 21st century (ie RCP8.5), the temperatures are forecast to continue increasing and by 2100 and reach around 4°C higher than the late 20th century levels. Concentrations of CO

_{2}in the atmosphere accelerate and reach 950 ppm by 2100. Based on the CO

_{2}trend there is a similarity between Global_ warming_01 with RCP 8.5 [39].

_{2}as compared to Global_Warming_01. So, Doubled_CO

_{2}data are downscaled by transform software.

### 3.2. Downscaling Validation

^{0}x 10

^{0}, so it considers the out-layer data for kriging interpolation. This study observed the variation in climate data of April 2017 after running ArcGIS and Transform software. Flash flood was the most hazardous incident in April 2017. A large part of the Sylhet region has been flooded in 2017. So, April 2017 was choosing to observe the variation in climate data. Fig. 3(b) and (c) show that both ArcGIS and Transform software generates the same temperature data for the study area.

### 3.3. Monthly and Yearly Variability of Temperature

^{2}) is higher in downscaled temperature than IPCC. Using CGCM3A2 and RCP 4.5 the maximum temperatures increases from 0.03°C–0.21°C in 2020 [41].

### 3.4. Annual Temperature Trend Analysis and Scatter Plot

^{−7}i.e. model downscaling performance is very good as [34].

### 3.5. Mann-Kendall Trend Test for Downscaled and Observed Temperature

*p*-value, which is computed using an exact method, is lower than the significance level alpha = 0.05, one should reject the null hypothesis, and accept the alternative hypothesis.The Kendall’s tau rank correlation coefficient is determined to show the sign of the relationship between time and the variables. Tau is first tested by the two-tailed test. Based on Kendall’s tau (

*τ*), the Mann-Kendall trend test is used to detect monotonic trends in the time series. The two-tailed Mann-Kendall test gives a significant result for both downscaled and observed temperature with the results for the tests of Kendall’s tau. Value of

*S*shows (positive) upward and (negative) downward trend.

### 3.6. Seasonal Mann-Kendall test of Downscaled and Observed Temperature

### 4. Conclusions

_{2}and Global_ Warming_01 can efficiently produce data on a global scale. Here, the kriging method was used to produce the missing data. Mean monthly temperature indicates a better correlation between the predictor and predictand variables for the period (1968–2017). The downscaled and observed values are more matching in monsoon season (June–September). During (2006–2020) the temperature changes is about 0.0165°C/year for downscaled and 0.00543°C/year for IPCC. Mann-Kendall Z tests for annual downscaled and IPCC during (2006–2020) show a positive trend. For downscaled temperature Z = 2.77 and sen’s slope 0.186 which means the trend is increasing significantly. For IPCC Z = 2.97 and sen’s slope 0.039 which is also increasing significantly. Downscaled annual temperature is also increased from starting period but it decreases after 2002 and further increases from 2004. Observed and downscaled average annual temperatures are changed by a factor of 0.054 and - 0.1077 degree Celsius. PBIAS of the downscaled temperature data are slightly underestimated of 6.05%. NSE and RAS values are 0.99 and 2.94 × 10

^{−7}. So the downscaled temperature is fitted well to the observed one. By Mann-Kendall trend test temperature changes are significant for both downscaled and observed results of p-value is less than alpha = 0.05. The study could have been far more extensive if more other scenarios of EdGCM data were used.